In essence, the robotic system is a complex information process system. But it is different from the computer, and its design must be considered together with the hardware and software. The systematic and comprehensive analysis of the intelligent service robot is still rare and not unified modelling methods and tools so far. Therefore, it is imperative to explore new methodological guidance for building mechanism. Our proposal tries to merge the strong points of the study methods mentioned above while minimizing their weak points, provide an open architecture for ISMR, and then explore the building mechanism of system for intelligent service mobile robot from metasynthesis's point of view.

The chapter is organized as follows. The features of analysis and design for ISMR are introduced in section 2. The metasynthesis of open architecture for ISMR is discussed in section 3. And the development model based on architecture is proposed in section 4. the system model based on Agent is provided in section 5. Finally, take a specific example to illustrate the efficiency and rationality of building mechanism in section 6. In addition, conclude and discuss this new methodological guidance of system analysis for ISMR and further research directions.

## **2. The features of system analysis and design for ISMR**

## **2.1 The difference between robot and computer**

Some people may ask, Robot system is ultimately embedded computer systems, we can use computer analysis methods to guide design robots, and compared to computer system, are there other special aspects in robotic? Robot system and logical relationship between the flow of information structure is calculated on the basis of computer platform, but the robot design and computer design is different.Because of the design objectives and the special constraints, the robot design is more complex than the computer . To successfully design the robot system, we must first understand the difference between the robot and the computer(as shown in Table.1), and then absorb and inherit of the computer system scientific analysis methods.

Overall, the robot is similar to organisms, which has a large number of complex sensors, and provide information or stimuli by them to understand and identify the environment, after that produce intelligent behavior through intelligent information processing to form a comprehensive closed-loop system. So the robot system design, not only need computer knowledge to support, but also need expertise and experience in robotics. Therefore, The combination of scientific methodology to build a computer system, robot designed common design theory, principle or a combination of domain modeling mechanism, are important condition for effectively building robotic systems.

## **2.2 The features of ISMR**

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82 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

autonomous mobile robot (Yavuz, 2007). A clearer view of the design approach based on function-oriented interdisciplinary were shown and the main functions of intelligent autonomous mobile robot were divided into Mobility, Navigation and Autonomy. Similarily, corresponding to design needs, five non-functional requirements including simple overall structure, cost effective, robust structure, intelligent and adaptive behaviour, and reliable operation were provided. Finally, those requirements were decomposed into smaller functions which could be performed by mechanical, electronic and software system through FD tree. In short, with the increased complexity of the system, the relationship between functional modules become more and more complex, so the overall system analysis

As for seeking for general architecture, many scholars tend to explore a common system reference model to reduce the duplication of design process. Architecture is the backbone of building complex intelligent systems (Ève coste-Manière, 2000). It can describe clearly that how the system is down into subsystem and how the subsystem achieves the overall function through the interaction of organic. Appropriate architecture make the robot system analysis become simple, so general architecture research has made some progress. For example, NASA and NBS's NASREM level reference model (Albus et al. 1989), Zeebo open architecture(Bogdan, 2006), ROACS(Real-time open-architecture control system) (Gu. J.s, 2004) and Daqing Wang's MRR (Modular and re-configurable robot) (Daqing wang, 2006), but they sill lack of effective methods of that how to analyze and design robotic system

On the whole, in order to solve the difficult problems, we need to consider it from two aspects. One is the research of the scientific, systematic analysis methods, which is related to robotic development; and the other is the research of architecture, which is related to

In essence, the robotic system is a complex information process system. But it is different from the computer, and its design must be considered together with the hardware and software. The systematic and comprehensive analysis of the intelligent service robot is still rare and not unified modelling methods and tools so far. Therefore, it is imperative to explore new methodological guidance for building mechanism. Our proposal tries to merge the strong points of the study methods mentioned above while minimizing their weak points, provide an open architecture for ISMR, and then explore the building mechanism of system for intelligent service mobile robot from metasynthesis's point of

The chapter is organized as follows. The features of analysis and design for ISMR are introduced in section 2. The metasynthesis of open architecture for ISMR is discussed in section 3. And the development model based on architecture is proposed in section 4. the system model based on Agent is provided in section 5. Finally, take a specific example to illustrate the efficiency and rationality of building mechanism in section 6. In addition, conclude and discuss this new methodological guidance of system analysis for ISMR and

Some people may ask, Robot system is ultimately embedded computer systems, we can use computer analysis methods to guide design robots, and compared to computer system, are

**2. The features of system analysis and design for ISMR**

**2.1 The difference between robot and computer**

and design still face many difficulties.

based on architecture systematically.

system model.

view.

further research directions.

For the robot, it's applications and the environment are very complex and there are still no unified theory and tools, so compared with the computer system, the robot can not be isolated only from the system of symbolic reasoning mechanisms, and it should be integrated and designed form the impact of three factors including body, environment and the task. In fact, the process of analysis and design is an integrated system, which turn these three system domain model to information mechanism.

In the intelligent service robot system, the robot body, the environment and the task work together, determine the robot's intelligent behavior. Therefore, when we conduct system analysis and design, the main line through the design of information systems includes not

Research on Building Mechanism of System for Intelligent Service Mobile Robot 85

People had provided much model architecture for intelligent robot system from different angles since early 1980's. The fact recognized by everyone is that three primitives including S (sensor), P (plan) and A (action) are the foundation of constructing architecture. S represents the ability to percept the internal and external state changes; P represents the ability to make decisions autonomously based on conditions, status and constraints; A represents the basic actions or behaviors of robot. According to this relationship between the three primitives, the structure of intelligent robotic system is classified three paradigms correspondingly including deliberative ( *SPA* ), reactive ( *S A* ) and hybrid (deliberative/reactive). Recently, the development of distributed artificial intelligence (DAI) had created new conditions for realizing hybrid paradigm. Parallelism, distribution, open and scalability will become new characteristics. Therefore, we divide previous reference architecture into categories which are traditional architecture and agent-based distributed

The traditional architecture is divided into three categories on the basis of three research paradigm (deliberative, reactive and hybrid). We sum up their characteristics from design thought, analysis methods, intelligent generation mechanism, and typical examples as

Category Deliberitive Reactive Hybrid

Analysis methods Top-down Bottom -up Metasynthesis

Symbol docytrine Behaviorism

Recently, more and more researches have introduced multi-agent system (MAS) to construct mobile robot system. Based on this theory, robot's frame looks like a network that is made of agent node. Agents are independent and of self-awareness, so the large-scale problems were expected to solve by this way. Therefore, in the module design, there are many researchers to replace modules with agent and the multi-agent system (MAS) mechanism based distributed control has provided a new train of though for designing ISMR. Now, more and more researchers use agent paradigm to solve complex problems. A multi-agent architecture for mobile robot control was provided by Kolp (Kolp 2006), he compared it to other classical structures such as control-loop, layered and task-trees and the results had shown that the

Simulate organisms perception-action response mechanism

Subsumption and reactive based on motor schema

Intergration of deliberative, reactive advantages

Comprehensive integration and connectionist

3T, AuRA, TCA and Sahira

Simulate human thinking such as introspection and deliberative

4-D/RCS

**3.1 The classification of intelligent robotic architecture**

architecture.

shown in Table 2.

Design thought

Intelligent generation mechanism

Typical examples Shakey, NASREM,

Table 2. The comparison of three research paradigm

**3.1.2 Agent-based distributed hybrid architecture**

**3.1.1 Traditional architecture**

only general information processing mechanisms, but also intelligent simulation or generation mechanism.Different Intelligent generation mechanism corresponding to different intelligent information processing method.

To sum up, intelligent robot with the characteristics of the following seven aspects(Zhu miaoliang, 2001).

F1: "survival" in a real the objective environment

F2: has some intelligence (intelligent generation mechanism)

F3: autonomous (independent of the problem-solving)

F4: has some ability to learn

F5: complex structure, Full range of levels

F6: belong to hybrid intelligent systems

Compared with general robot, ISMR is developed with the aim to perform operations in special no-industrial tasks, so it has some new features as follows excepting above(Xie wei, 2010).

F7: intelligent demanding

F8:module versatility

F9:friendly human-robot interaction

F10:secure high-performance

As can be seen from the above characteristics, building intelligent mobile robot is a complex process of comprehensive integration. With a variety of subjects (control theory, operations research, developmental psychology and systems theory, etc.) of the theories and methods to the penetration of artificial intelligence, comprehensive and integrated approach is an effective way to analyze the design of intelligent mobile robot system. Summarizing the design of robotic systems in recent years, according to Mr. Qian Xuesen's comprehensive integrated methodology(Cao Longbin, Dai Ruwei, 2008), comprehensive and integrated strategy of the robot can be summarized as the top-down and bottom-up, system abstraction and simulation complementary, more angle comprehensive modeling approach, multidisciplinary, human (qualitative) and robots (quantitative) combination, complementary non-functional and functional division and from qualitative analysis to quantitative modeling and integration analysis.

## **3. The open reference architecture of ISMR**

If we want to build an intelligent robotic system, the first step is to determine the appropriate architecture. The architecture includes information processing and control system, which ensures reasonable coordination, openness, and scalability of robotic system. The same as the brain, the architecture is the platform "body" for realizing intelligent behaviours, so new developments in artificial intelligence can provide some new ideological for designing ISMR. Actually, robotic system design involves two aspects of engineering and science. If there is no systematic analysis and design, any design methods based on "repeated optimization" and "trial and error" are difficult to design the overall behaviour offline. In this section, the typical reference architecture in the past 30 years are classified and summed up systematically from system analysis perspective, and then some common principles for design architecture are concluded on the basis of previous classified research. Finally, the open reference architecture of ISMR is provided.

## **3.1 The classification of intelligent robotic architecture**

People had provided much model architecture for intelligent robot system from different angles since early 1980's. The fact recognized by everyone is that three primitives including S (sensor), P (plan) and A (action) are the foundation of constructing architecture. S represents the ability to percept the internal and external state changes; P represents the ability to make decisions autonomously based on conditions, status and constraints; A represents the basic actions or behaviors of robot. According to this relationship between the three primitives, the structure of intelligent robotic system is classified three paradigms correspondingly including deliberative ( *SPA* ), reactive ( *S A* ) and hybrid (deliberative/reactive). Recently, the development of distributed artificial intelligence (DAI) had created new conditions for realizing hybrid paradigm. Parallelism, distribution, open and scalability will become new characteristics. Therefore, we divide previous reference architecture into categories which are traditional architecture and agent-based distributed architecture.

## **3.1.1 Traditional architecture**

Mobile Robots – Control Architectures,

84 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

only general information processing mechanisms, but also intelligent simulation or generation mechanism.Different Intelligent generation mechanism corresponding to

To sum up, intelligent robot with the characteristics of the following seven aspects(Zhu

Compared with general robot, ISMR is developed with the aim to perform operations in special no-industrial tasks, so it has some new features as follows excepting above(Xie wei,

As can be seen from the above characteristics, building intelligent mobile robot is a complex process of comprehensive integration. With a variety of subjects (control theory, operations research, developmental psychology and systems theory, etc.) of the theories and methods to the penetration of artificial intelligence, comprehensive and integrated approach is an effective way to analyze the design of intelligent mobile robot system. Summarizing the design of robotic systems in recent years, according to Mr. Qian Xuesen's comprehensive integrated methodology(Cao Longbin, Dai Ruwei, 2008), comprehensive and integrated strategy of the robot can be summarized as the top-down and bottom-up, system abstraction and simulation complementary, more angle comprehensive modeling approach, multidisciplinary, human (qualitative) and robots (quantitative) combination, complementary non-functional and functional division and from qualitative analysis to quantitative

If we want to build an intelligent robotic system, the first step is to determine the appropriate architecture. The architecture includes information processing and control system, which ensures reasonable coordination, openness, and scalability of robotic system. The same as the brain, the architecture is the platform "body" for realizing intelligent behaviours, so new developments in artificial intelligence can provide some new ideological for designing ISMR. Actually, robotic system design involves two aspects of engineering and science. If there is no systematic analysis and design, any design methods based on "repeated optimization" and "trial and error" are difficult to design the overall behaviour offline. In this section, the typical reference architecture in the past 30 years are classified and summed up systematically from system analysis perspective, and then some common principles for design architecture are concluded on the basis of previous classified research.

different intelligent information processing method.

F1: "survival" in a real the objective environment

F5: complex structure, Full range of levels F6: belong to hybrid intelligent systems

F2: has some intelligence (intelligent generation mechanism) F3: autonomous (independent of the problem-solving)

miaoliang, 2001).

2010).

F4: has some ability to learn

F7: intelligent demanding F8:module versatility

F9:friendly human-robot interaction F10:secure high-performance

modeling and integration analysis.

**3. The open reference architecture of ISMR**

Finally, the open reference architecture of ISMR is provided.

The traditional architecture is divided into three categories on the basis of three research paradigm (deliberative, reactive and hybrid). We sum up their characteristics from design thought, analysis methods, intelligent generation mechanism, and typical examples as shown in Table 2.

Table 2. The comparison of three research paradigm

## **3.1.2 Agent-based distributed hybrid architecture**

Recently, more and more researches have introduced multi-agent system (MAS) to construct mobile robot system. Based on this theory, robot's frame looks like a network that is made of agent node. Agents are independent and of self-awareness, so the large-scale problems were expected to solve by this way. Therefore, in the module design, there are many researchers to replace modules with agent and the multi-agent system (MAS) mechanism based distributed control has provided a new train of though for designing ISMR. Now, more and more researchers use agent paradigm to solve complex problems. A multi-agent architecture for mobile robot control was provided by Kolp (Kolp 2006), he compared it to other classical structures such as control-loop, layered and task-trees and the results had shown that the

Research on Building Mechanism of System for Intelligent Service Mobile Robot 87

Sensors(S) Actuators(A)

(p)

Fig. 1. The open and space-oriented reference architecture of ISMR with learning mechanism As can be seen from Fig.1, in the space of the layered architecture, both vertical and horizontal dimensions are used to build the analytical framework. It is from the lower physical layer, behaviours layer, coordination layer, plan layer to task layer in vertical dimension, and from the left to right, it is divided into information, intelligence and knowledge conversion mechanism. The physical layer mainly includes sensors and actuators.  Under this architecture, it is easy to form a comprehensive integrated system of division and interaction. From low to high, it shows the intelligent space hierarchy and intelligence are increased gradually. However, from the left to right, it reflects the intelligence produced by the common core mechanism. Therefore, this architecture has reflected the full range of intelligent frame structure of system content.

Each robot has its own architecture, like everyone has the body. Good architecture not only makes the problem easier to solve, but also is more conducive to the integration of complex subsystems. This is the purpose of our study architecture. The traditional design of robotic

**4. The development model of ISMR based on architecture**

B1 B2

level1 level2

Leveln

level1 level2

Leveln

Bn

P1

P2

Pn

Information intelligence converter knowledge

Task decomposition

Coordination

Arbiter

IS

learning and evaluation

knowledge learning

learning and supervise Intelligence are improving

behavior learning

Environment

Task layer

Plan layer

Coordination

layer

Deliberative paradigm

top-down

Reactivve paradigm

Bottom-up

Physical layer

Behaviours layer

local information understand

current information monitor

Globle fusion

Task receive or release

structure-in-5 and joint-venture based on MAS had obvious advantages. Grelle (Grelle 2007) used the agent paradigm to design very complex control strategy through multi-objective, fuzzy c-means, and genetic algorithms optimization. Under the hybrid architecture, the traditional control model was extended in agent-based distributed system. The classical architecture including the integration architecture of MAS and collaborative (Bianca, 2008), SC-agent architecture (Posadas, 2009) . Because the agent can decouple hardware and software, some methods of soft computer may be used to address cooperation and competition between agents. The approach of neural network based on evolutionary and reinforcement to design agent were presented in (Strnislav, 2010), and the results had shown Q learning algorithm was more adaptative for agent.

## **3.2 Discussion of common principles**

Through classification and summary of past typical architecture, we not only can absorb previous experience and lessons, but also induct some common design principles for guiding follow-up study. Inspired by Pfeifer's new ideas of embodied cognition ( Pfeifer, 2005), we can understand intelligence system by building. Historical experience have also proved that some new ideas or thought emerge from building a real physical system. Therefore, the general methodology on the basis of previous experiences and views are expected to get. And then, borrowing ideas from Pfeifer, we have concluded six common design principles for constructing architecture of intelligent robot system (Xie wei 2010).

Although the robot intelligence is so mysterious that people understand it only from a different side, we still can conclude some common design principles which are guidelines for designing efficiently robot system.

## **3.3 The open reference architecture of ISMR**

From the development of architecture in last 30 years, we find that hybrid paradigm is effective design methods for constructing intelligent robot system. The computing models are the basis of robotic analysis and design, which have changed from process-oriented, objected-oriented and component-oriented to agent-oriented. And agent-oriented distributed hybrid architecture has more obvious advantages such as modularity, integration capability, scalability and openness than traditional architecture. But no matter which type of computing model is selected, it must have open reference architecture, only in this way, the design of ISMR is expected to change from code reuse, module reuse, component reuse to architecture reuse.

Combining six common principles above to metasynthesis, and referencing to common core mechanism of "information-Knowledge-intelligence converter", an open and space-oriented reference architecture of ISMR with learning mechanism is presented in this paper as shown in Fig.1.

Mobile Robots – Control Architectures,

86 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

structure-in-5 and joint-venture based on MAS had obvious advantages. Grelle (Grelle 2007) used the agent paradigm to design very complex control strategy through multi-objective, fuzzy c-means, and genetic algorithms optimization. Under the hybrid architecture, the traditional control model was extended in agent-based distributed system. The classical architecture including the integration architecture of MAS and collaborative (Bianca, 2008), SC-agent architecture (Posadas, 2009) . Because the agent can decouple hardware and software, some methods of soft computer may be used to address cooperation and competition between agents. The approach of neural network based on evolutionary and reinforcement to design agent were presented in (Strnislav, 2010), and the results had shown

Through classification and summary of past typical architecture, we not only can absorb previous experience and lessons, but also induct some common design principles for guiding follow-up study. Inspired by Pfeifer's new ideas of embodied cognition ( Pfeifer, 2005), we can understand intelligence system by building. Historical experience have also proved that some new ideas or thought emerge from building a real physical system. Therefore, the general methodology on the basis of previous experiences and views are expected to get. And then, borrowing ideas from Pfeifer, we have concluded six common design principles for constructing architecture of intelligent robot system

Although the robot intelligence is so mysterious that people understand it only from a different side, we still can conclude some common design principles which are guidelines

From the development of architecture in last 30 years, we find that hybrid paradigm is effective design methods for constructing intelligent robot system. The computing models are the basis of robotic analysis and design, which have changed from process-oriented, objected-oriented and component-oriented to agent-oriented. And agent-oriented distributed hybrid architecture has more obvious advantages such as modularity, integration capability, scalability and openness than traditional architecture. But no matter which type of computing model is selected, it must have open reference architecture, only in this way, the design of ISMR is expected to change from code reuse, module reuse,

Combining six common principles above to metasynthesis, and referencing to common core mechanism of "information-Knowledge-intelligence converter", an open and space-oriented reference architecture of ISMR with learning mechanism is presented in this paper as shown

Q learning algorithm was more adaptative for agent.

Principles of modular division based on S, P and A

 Coordination principle between deliberative and reactive Principle of open-hardware based computer platform

**3.2 Discussion of common principles**

 Hierarchical principle of architecture High cohesion and low coupling Design principle of redundancy

for designing efficiently robot system.

component reuse to architecture reuse.

in Fig.1.

**3.3 The open reference architecture of ISMR**

(Xie wei 2010).

Fig. 1. The open and space-oriented reference architecture of ISMR with learning mechanism

As can be seen from Fig.1, in the space of the layered architecture, both vertical and horizontal dimensions are used to build the analytical framework. It is from the lower physical layer, behaviours layer, coordination layer, plan layer to task layer in vertical dimension, and from the left to right, it is divided into information, intelligence and knowledge conversion mechanism. The physical layer mainly includes sensors and actuators.  Under this architecture, it is easy to form a comprehensive integrated system of division and interaction. From low to high, it shows the intelligent space hierarchy and intelligence are increased gradually. However, from the left to right, it reflects the intelligence produced by the common core mechanism. Therefore, this architecture has reflected the full range of intelligent frame structure of system content.

#### **4. The development model of ISMR based on architecture**

Each robot has its own architecture, like everyone has the body. Good architecture not only makes the problem easier to solve, but also is more conducive to the integration of complex subsystems. This is the purpose of our study architecture. The traditional design of robotic

Research on Building Mechanism of System for Intelligent Service Mobile Robot 89

goals (performance indicators). Has been proved(Hejian, 2004), 70% of software errors are that the result of requirements analysis is not clear, so pre- requirements analysis for the problem-solving and building system are critical. Description of the problem refers to analyze problems needed to slove clearly. Define the scope of the real refers to determine the system's objects and the environment. Only on the basis of two points of rigorous analysis in front, we determine the appropriate performance indicators. This is the same as the concept of three elements of the principle of "niche" (Pfeifer, 2005) . That is to say, on the basis of the expected behavior of the robot, we define the living environment of the robot, or applications. Such as service robots and industrial robots, 'niche' have different requirements, service robots are mainly used in homes, hotels and other more services social environment, but industrial robots are used in specific occasions such as factory

It can be seen from Fig.2, for demand analysis, there are two important external factors, including qualitative experience and knowledge or some common principle which are integrated into the design requirements analysis and combined qualitative design and specific areas of application requirements analysis together to solve human-computer collaborative problem. In the problem solving process, how to divide the problem is the key problem. Function-oriented classification method are easy to decouple hardware and software, and easy to analogy organisms, so it is generally recognized by every one. This method considers in conjunction with hardware and software to simplify system integration difficulties. But in the practice, the design of building robot system also face a number of non-functional requirements such as cost, robustness, operability and openness. When the three important aspects of requirements analysis is clear, we can use the existing experience or knowledge to identify problem-solving approach. When the results of the requirements analysis are converted into a functional description (the main function of the robot needs to be done) and non-functional description (constraints and performance indicators), we can see that requirements analysis is multidimensional. In the process of requirements analysis, regardless of how complex the problem to be solved, the problem can be driven in the framework of a causal relationship between the division. From different angles, this section provides three sets of functional design space for robot system (FD) to visually describe the

*FD Fr,NFr ,Ds* (1)

environment which only need relatively simple interactions.

In which: *Fr F ,F ,...,Fn* 1 2 Functional requirements set

system functional requirements are expressed as a set:

*DS Ds ,Ds ,...,Dsn* 1 2 Set of design specifications

*NFr NF ,NF ,...,NFn* 1 2 Collection of non-functional requirements

For example, in this analysis mode, Hakan Yavuz's intelligent autonomous mobile robot

*Fr Mobilit y,Navigation,Autonomy* .

Of course, the requirements analysis is not static, on the one hand, before the next system design it should assessed and changed repeatedly, and then be formed to documented description normative; on the hand other, after systems construction is built completely and

needs analysis results.

*n* is a natural number

systems' development life cycle is divided into problem areas and the determines of the objective function, system analysis, system design, system implementation and system testing five stages(Zhu Miaoliang 2001). As the complexity of robotic systems are increased, it is difficult in the requirements analysis phase of an overall understanding of the system, so the actual development requires repeated changes and additions, more coupled subsystems, more experience components, lower development efficiency. Once the changes in demand or changes in the field of environment, much places needed to be modified or redesigned. The architecture as the backbone of the system, can help us build from the system point of view of system modules and intelligent interaction between this organization. Therefore, the section integrates with complex systems integration strategy, based on the architecture of the software engineering analysis method (Zhang Youshen, 2004 Hejian, 2004), and proposes development model of intelligent robot system based on architecture as shown in Fig.2

Fig. 2. Development model of intelligent robot system based on architecture

In Fig.2, the core is the architecture design. Architecture likes a bridge, which connect the robot system problem solving, needs analysis and information system. Inspired by the software architecture research ideas, the development model of Fig. 1 is divided into four design space, which are functional design (FD)space based on demand analysis, architecture design (AD) space based on architecture, system design (SD) space based on system implementation and system evolution (SE) space based on evolution( including testing, optimization and iterative modification), which are functional design (FD)space based on demand analysis, architecture design (AD) space based on architecture, system design (SD) space based on system implementation and system evolution (SE) space based on evolution( including testing, optimization and iterative modification), and finally the open robotic system are formed and some design models or components are built, then the development life cycle is completed. Compared to traditional system development model, it not only reflects the comprehensive and integrated methodology from the qualitative to quantitative, but also makes the system design from code reuse, reuse components to reusable design patterns, and improve the system development efficiency.

## **4.1 Requirements analysis**

Robotic system requirements analysis include three aspects which are the problem description and classification (solve problems), determine the scope (constraints) and design Mobile Robots – Control Architectures,

System evolution Open robotic system

General framework to extract

Component library or design patterns

88 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

systems' development life cycle is divided into problem areas and the determines of the objective function, system analysis, system design, system implementation and system testing five stages(Zhu Miaoliang 2001). As the complexity of robotic systems are increased, it is difficult in the requirements analysis phase of an overall understanding of the system, so the actual development requires repeated changes and additions, more coupled subsystems, more experience components, lower development efficiency. Once the changes in demand or changes in the field of environment, much places needed to be modified or redesigned. The architecture as the backbone of the system, can help us build from the system point of view of system modules and intelligent interaction between this organization. Therefore, the section integrates with complex systems integration strategy, based on the architecture of the software engineering analysis method (Zhang Youshen, 2004 Hejian, 2004), and proposes development

model of intelligent robot system based on architecture as shown in Fig.2

Architecture design

Fig. 2. Development model of intelligent robot system based on architecture

reusable design patterns, and improve the system development efficiency.

Basic structure of prototype

In Fig.2, the core is the architecture design. Architecture likes a bridge, which connect the robot system problem solving, needs analysis and information system. Inspired by the software architecture research ideas, the development model of Fig. 1 is divided into four design space, which are functional design (FD)space based on demand analysis, architecture design (AD) space based on architecture, system design (SD) space based on system implementation and system evolution (SE) space based on evolution( including testing, optimization and iterative modification), which are functional design (FD)space based on demand analysis, architecture design (AD) space based on architecture, system design (SD) space based on system implementation and system evolution (SE) space based on evolution( including testing, optimization and iterative modification), and finally the open robotic system are formed and some design models or components are built, then the development life cycle is completed. Compared to traditional system development model, it not only reflects the comprehensive and integrated methodology from the qualitative to quantitative, but also makes the system design from code reuse, reuse components to

Robotic system requirements analysis include three aspects which are the problem description and classification (solve problems), determine the scope (constraints) and design

System implementation

General purpose computing platform

Iterative incremental correction

Requirements analysis

**4.1 Requirements analysis**

Knowledge experience

Changes in demand

Build problems of robot system goals (performance indicators). Has been proved(Hejian, 2004), 70% of software errors are that the result of requirements analysis is not clear, so pre- requirements analysis for the problem-solving and building system are critical. Description of the problem refers to analyze problems needed to slove clearly. Define the scope of the real refers to determine the system's objects and the environment. Only on the basis of two points of rigorous analysis in front, we determine the appropriate performance indicators. This is the same as the concept of three elements of the principle of "niche" (Pfeifer, 2005) . That is to say, on the basis of the expected behavior of the robot, we define the living environment of the robot, or applications. Such as service robots and industrial robots, 'niche' have different requirements, service robots are mainly used in homes, hotels and other more services social environment, but industrial robots are used in specific occasions such as factory environment which only need relatively simple interactions.

It can be seen from Fig.2, for demand analysis, there are two important external factors, including qualitative experience and knowledge or some common principle which are integrated into the design requirements analysis and combined qualitative design and specific areas of application requirements analysis together to solve human-computer collaborative problem. In the problem solving process, how to divide the problem is the key problem. Function-oriented classification method are easy to decouple hardware and software, and easy to analogy organisms, so it is generally recognized by every one. This method considers in conjunction with hardware and software to simplify system integration difficulties. But in the practice, the design of building robot system also face a number of non-functional requirements such as cost, robustness, operability and openness. When the three important aspects of requirements analysis is clear, we can use the existing experience or knowledge to identify problem-solving approach. When the results of the requirements analysis are converted into a functional description (the main function of the robot needs to be done) and non-functional description (constraints and performance indicators), we can see that requirements analysis is multidimensional. In the process of requirements analysis, regardless of how complex the problem to be solved, the problem can be driven in the framework of a causal relationship between the division. From different angles, this section provides three sets of functional design space for robot system (FD) to visually describe the needs analysis results.

$$FD = \{Fr, NFr, Ds\} \tag{1}$$

In which: *Fr F ,F ,...,Fn* 1 2 Functional requirements set

$$\begin{aligned} n &\quad \text{is a natural number} \\ NFr &= \{NF1, NF2, \dots, NFn\} & \text{Cl collection of non-functional requirements} \\ DS &= \{Ds1, Ds2, \dots, Dsn\} & \text{Set of design specifications} \end{aligned}$$

For example, in this analysis mode, Hakan Yavuz's intelligent autonomous mobile robot system functional requirements are expressed as a set:

$$Fr = \{\text{Mobiliity}, \text{Navigation}, \text{Autonony}\}.$$

Of course, the requirements analysis is not static, on the one hand, before the next system design it should assessed and changed repeatedly, and then be formed to documented description normative; on the hand other, after systems construction is built completely and

Research on Building Mechanism of System for Intelligent Service Mobile Robot 91

Building abstract and appropriate model help us to analyze the integrated mechanism and intelligence generation mechanism of architecture. It's easy to do dynamic optimized analysis, and also to predict the results of running in the environment. Recent cognitive structure, neurological psychological structure and architecture based on learning and emotion all can be regarded as evolutions of these three basic architectures. Therefore, if we consider the basic architecture as the study prototype, the system model is easy to

From a macro point of view, the level of robot intelligent will be reflected by intelligent behavior finally. While the most important factor of affecting intelligent behaviour is the sense of the environment (S), so if we take IB (intelligent behavior) as output, the environment as input, then the basic architecture of a simplified model is expressed as:

Among them, *f* means mapping mechanism from environment to intelligent behaviour. In the intelligent service robot system, the environmental status information are divided into two categories: external and internal environment. External environment includes robot running ecological environment and task information released by human or other robot; internal environment includes information about robot's understanding of environment (global and local) and introspection information (dynamic cognitive knowledge, learning, evaluation, memory information, etc.). Intelligence mainly is showed as intelligent strategy selection and the generation of correct behavior. In different environment, the mapping mechanism *f* is different. So based on functional design space requirements and the basic architecture, intelligent robotic systems analysis model will be built. System architecture

According to this conversion from function design space to structure space, how to make mapping mechanism? This mapping mechanism must be met the real-time requirements by relatively simple linear transformation (including general and special) or fuzzy query table. Linear transformation method describes model *f* by a linear mathematical model or ARMAX(Nehmzow, 2006)system identification method. Method of fuzzy query table *Q s, a* achieves nonlinear *f* mapping mechanism through off-line fuzzy logic, and complex behavior will be real-time and dynamic completed, looking up table online. This analysis based on bottom-up and interaction principle based on perception-reaction have laid the foundation for the realization of reactive intelligent behavior. It is noteworthy that, in the lowest level of response mode, closed-loop control system as chief, such as motion control system, has the strongest instantaneity, can also be attributed to the special analysis model:

*S A* . The mapping mechanism is designed with control system design theory.

Deliberative style basic architecture ( *SPA* ), is came true through intelligent information processing mechanism (mapping or policy). Planning includes task decomposition, scheduling decisions, global and local path planning. Online and off-line planning are coexistence. On-line planning includes environmental awareness, sensor fusion, task decomposition and scheduling, cooperative control and real-time path planning;

*f : E IB* (2)

*AD A , A , A , ...,A* <sup>123</sup> *<sup>n</sup>* (3)

discussed.

design space ( *AD* ) can be expressed as a set:

*Ai* means the basic structure of subsystem

fulfilled, changes are caused by increasing or reducing part of the functional and nonfunctional requirements. So the analysis needs to consider the dynamic update mechanism. These two external factors form requirements analysis process of comprehensive and integrated robotic system.

## **4.2 The design of architecture**

According to IEEE610.12-1990 for the definition of software architecture: "Architecture is a system basic structure which contents component, the relationship among the components, the relationship between component and environment and a principle of guiding the design and evolution of the content above. " It can be seen that, components, connectors, environment and principle are of its constituent elements. At present there are not formed accepted general definition, but "Software Architecture = {components, connectors, constraints}" is generally reach a consensus. In the robot system, reference to the definition of software architecture studied by Bass, Shaw, Boehm and so on, the architecture can be understood as an abstract system specification, mainly used to describe interconnect and the set of various functional components, also includes guiding design principle. In the system development model based on robot architecture, the main task of designing architecture is to design the content based on function, to make out the content or basic structure and achieve these content and the relationship between them clearly, to make out system analysis model, to form the structure of the system design space ( *AD* ).

## **4.2.1 The abstract of system component**

Robot academic generally accepted that, functions of robot are divided into three categories: sense (S), plan (P) and achievement (A).From the existing literature, it has not find more universal significance than these three functional classification, meanwhile S, P and A was also seen as the three primitives of building a robot system. Although the learning (L) to build intelligent systems as a primitive is still debated, in this section, we consider the learning paradigm as the fourth one apply to the design of the system, because learning is the most important manifestation of intelligence and integrated in the first three primitives. According to "By constructing to understand intelligent systems" and the relationship between these types of research paradigms, the robot basic system, combining to intelligent organization, is abstracted as three species: deliberate, reaction and hybrid (deliberate/reaction), corresponding to the three basic architecture. The characteristics of these three basic architectures are shown in Table 2. According to these research ideas, the components can be replaced by these four primitives. Intelligent organization form and architecture design of system are united and then to the component model of building intelligent systems are achieved.

Throughout development of research on intelligent abstraction mechanisms recent years, it experienced from the symbolic intelligence (the physical symbol hypothesis as the representative), connected intelligence (the artificial neurons as the representative), on-site intelligence (perception - action mechanism as the representative) to the community smart (Agent, represented in the social interaction, organization and emergence, etc.). As can be seen from Table 2, the basic architecture design and intelligent abstraction mechanism are integrated to consider.

### **4.2.2 Basic architecture analysis model**

System modeling is the core of the architecture, the environment and the objects intelligent mobile robot systems to deal with are very complex, there is no unified theory and tools. Mobile Robots – Control Architectures,

90 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

fulfilled, changes are caused by increasing or reducing part of the functional and nonfunctional requirements. So the analysis needs to consider the dynamic update mechanism. These two external factors form requirements analysis process of comprehensive and

According to IEEE610.12-1990 for the definition of software architecture: "Architecture is a system basic structure which contents component, the relationship among the components, the relationship between component and environment and a principle of guiding the design and evolution of the content above. " It can be seen that, components, connectors, environment and principle are of its constituent elements. At present there are not formed accepted general definition, but "Software Architecture = {components, connectors, constraints}" is generally reach a consensus. In the robot system, reference to the definition of software architecture studied by Bass, Shaw, Boehm and so on, the architecture can be understood as an abstract system specification, mainly used to describe interconnect and the set of various functional components, also includes guiding design principle. In the system development model based on robot architecture, the main task of designing architecture is to design the content based on function, to make out the content or basic structure and achieve these content and the relationship between them clearly, to make out system

Robot academic generally accepted that, functions of robot are divided into three categories: sense (S), plan (P) and achievement (A).From the existing literature, it has not find more universal significance than these three functional classification, meanwhile S, P and A was also seen as the three primitives of building a robot system. Although the learning (L) to build intelligent systems as a primitive is still debated, in this section, we consider the learning paradigm as the fourth one apply to the design of the system, because learning is the most important manifestation of intelligence and integrated in the first three primitives. According to "By constructing to understand intelligent systems" and the relationship between these types of research paradigms, the robot basic system, combining to intelligent organization, is abstracted as three species: deliberate, reaction and hybrid (deliberate/reaction), corresponding to the three basic architecture. The characteristics of these three basic architectures are shown in Table 2. According to these research ideas, the components can be replaced by these four primitives. Intelligent organization form and architecture design of system are united and then to the component model of building

Throughout development of research on intelligent abstraction mechanisms recent years, it experienced from the symbolic intelligence (the physical symbol hypothesis as the representative), connected intelligence (the artificial neurons as the representative), on-site intelligence (perception - action mechanism as the representative) to the community smart (Agent, represented in the social interaction, organization and emergence, etc.). As can be seen from Table 2, the basic architecture design and intelligent abstraction mechanism are

System modeling is the core of the architecture, the environment and the objects intelligent mobile robot systems to deal with are very complex, there is no unified theory and tools.

analysis model, to form the structure of the system design space ( *AD* ).

integrated robotic system.

**4.2 The design of architecture**

**4.2.1 The abstract of system component**

intelligent systems are achieved.

**4.2.2 Basic architecture analysis model**

integrated to consider.

Building abstract and appropriate model help us to analyze the integrated mechanism and intelligence generation mechanism of architecture. It's easy to do dynamic optimized analysis, and also to predict the results of running in the environment. Recent cognitive structure, neurological psychological structure and architecture based on learning and emotion all can be regarded as evolutions of these three basic architectures. Therefore, if we consider the basic architecture as the study prototype, the system model is easy to discussed.

From a macro point of view, the level of robot intelligent will be reflected by intelligent behavior finally. While the most important factor of affecting intelligent behaviour is the sense of the environment (S), so if we take IB (intelligent behavior) as output, the environment as input, then the basic architecture of a simplified model is expressed as:

$$f: \mathcal{E} \to \mathcal{I} \mathcal{B} \tag{2}$$

Among them, *f* means mapping mechanism from environment to intelligent behaviour. In the intelligent service robot system, the environmental status information are divided into two categories: external and internal environment. External environment includes robot running ecological environment and task information released by human or other robot; internal environment includes information about robot's understanding of environment (global and local) and introspection information (dynamic cognitive knowledge, learning, evaluation, memory information, etc.). Intelligence mainly is showed as intelligent strategy selection and the generation of correct behavior. In different environment, the mapping mechanism *f* is different. So based on functional design space requirements and the basic architecture, intelligent robotic systems analysis model will be built. System architecture design space ( *AD* ) can be expressed as a set:

$$AD = \begin{pmatrix} A\_1 \ \ A\_2 \ \ A\_3 \ \dots \ A\_n \end{pmatrix} \tag{3}$$

#### *Ai* means the basic structure of subsystem

According to this conversion from function design space to structure space, how to make mapping mechanism? This mapping mechanism must be met the real-time requirements by relatively simple linear transformation (including general and special) or fuzzy query table. Linear transformation method describes model *f* by a linear mathematical model or ARMAX(Nehmzow, 2006)system identification method. Method of fuzzy query table *Q s, a* achieves nonlinear *f* mapping mechanism through off-line fuzzy logic, and complex behavior will be real-time and dynamic completed, looking up table online. This analysis based on bottom-up and interaction principle based on perception-reaction have laid the foundation for the realization of reactive intelligent behavior. It is noteworthy that, in the lowest level of response mode, closed-loop control system as chief, such as motion control system, has the strongest instantaneity, can also be attributed to the special analysis model: *S A* . The mapping mechanism is designed with control system design theory.

Deliberative style basic architecture ( *SPA* ), is came true through intelligent information processing mechanism (mapping or policy). Planning includes task decomposition, scheduling decisions, global and local path planning. Online and off-line planning are coexistence. On-line planning includes environmental awareness, sensor fusion, task decomposition and scheduling, cooperative control and real-time path planning;

Research on Building Mechanism of System for Intelligent Service Mobile Robot 93

precision is decreasing. The subsumption structure of Brooks, with that the high-level tolerant and include the low-level behavior and have the characteristics of stacked layers, this hierarchy has provided a convenient for building robotic system. In this framework, or the distribution of each layer, the component form of organization may be divided into centralized, distributed and hybrid (centralized/ distributed). The intelligence are increasing with the level of compatibility, from low to high. The comparison of three kinds

Category Centralized Distributed Hybrid

In the process of function design space are converted into the structure designing space, the functional set is converted into the basic architecture by the primitive. Then the nonfunctional requirements and performance indicators are reflected in the architecture of the constraints. If we use these components to achieve the function of Fr set, the connection between components and the algorithm can reflect the constraints of Ds and NFr set. Thus, according to functional and non functional requirements, we determine the basic structure of the analysis model, and select appropriate mapping mechanism to realize, and achieve a function designing space completely transforms into a subset of structure designing space (basic structure). Then the problems are transformed into that how to use these form the

From the view of system theory, complex systems have a Character of "hierarchy" and typically have a structure of modular, so we use the method of hierarchical – aggregation and block-integrate modeling to solve this problem. Nearly 30 years analysis research of robot architecture shows that hybrid architecture is still dominant when the basic model and its organization are identified, we use the comprehensive integrated spatial structure of

The robotic system building process are descried simply as following after the AD set of structure design space is determined, we can use the method of combination modeling to

C2 C3 C0 C1 C2 C3

Bus

Every component has local information

Distributed management and Heterogeneous components can be integrated

Difficult to deal with global information and need collaboration and competition

C0 C1

Balance the first two

Metasynthesis

Design complexity and diffcult to integrate

C2 C3

Bus

of organization for designing system in Fig.1 are described in Table 3.

C0

C0 plans and schedulings global information

Facilitate unified planning and management

Single information flow and weak rebustness

Table 3. Comparison of three organized manner based on component

**4.2.4 The comprehensive integration of structure space**

basic structure of the whole system model.

intelligent service as Fig.1 to build system gradually.

Describe

Scheduling

Advantages

Disadvatages

Ci is component C1

off-line planning includes building a library of knowledge and experience, the identification of demonstration representation and trajectory planning. The generation of intelligent strategies come from planning. Driven by goals or tasks, the sensor information are fused and processed into knowledge, intelligent strategies are self-generated and then intelligent behaviours are expected. The traditional deliberative design focuses on logical reasoningsearch. In recent years, some soft calculation methods (such as fuzzy logic, neural networks, evolutionary computation, etc.) are applied to the deliberative reasoning design which reflect the comprehensive integration trend. For example, ANN hybrid architecture (Xu Yuru, 2007) used by XU Yu-ru combined with symbolic reasoning and artificial neural network, fully embodied the strengths of complementary of fusion.

Of course, if you reuse the basic architecture, then the corresponding mapping mechanism can be reused. This gives convenience to system analysis and design. For example, obstacle avoidance and along the wall of the differential behavior robot studied by Joseph (Jones, J.L. 2004), the same type of obstacle avoidance sensors are equipped on robot's left and right sides. He took mapping mechanism as common linear transformation model *A* , provided us with general ideas based on behavior analysis model. Simply select the appropriate transformation matrix *A* , reactive intelligent behavior can be achieved. Intelligent behavior IB matrix expressed as:

$$IB = \begin{bmatrix} Tr \ R & Tri \end{bmatrix} \tag{4}$$

*Tr* : Translation speed; *R* : Rotation speed; *Tri* : Trigger flag

Assuming the sensor input *El* and *Er* , The matrix is expressed as:

$$E = \begin{bmatrix} EI \ Er & 1 \end{bmatrix} \tag{5}$$

Status signal: 1 indicates that this behavior is triggered, 0 indicates that this behavior is not triggered

Then there exists a common linear transformation matrix *T* .Such that *IB ET* .

In its light-seeking robot design, if *Tr V* , *V* means speed navigation; *R K(El Er )* , *K* for the normalization factor, the corresponding linear transformation matrix is:

$$T = \begin{bmatrix} \mathbf{0} & \mathbf{K} & \mathbf{0} \\ \mathbf{0} & -\mathbf{K} & \mathbf{0} \\ V & \mathbf{0} & \mathbf{1} \end{bmatrix} \tag{6}$$

## **4.2.3 Architecture of the spatial distribution**

Currently, the concept that intelligent system should be layered has been widely recognized by everyone. The four-model computer systems and the seven-model computer network architecture, fully reflects the level of modeling ideas. Intelligent robot system with multilevel, three-dimensional and flexible, dynamic organizational structure and other features, are the same as cognitive scientists believe that animals have different levels of spatial capacity (reflection, integration, learning and cognitive). Therefore, the simulated biological process of intelligent systems also run through this philosophy. For example, the hierarchical structure of Saridis, level from low to high and intelligent increasing, the control Mobile Robots – Control Architectures,

*IB Tr R Tri* (4)

*E El Er* 1 (5)

(6)

92 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

off-line planning includes building a library of knowledge and experience, the identification of demonstration representation and trajectory planning. The generation of intelligent strategies come from planning. Driven by goals or tasks, the sensor information are fused and processed into knowledge, intelligent strategies are self-generated and then intelligent behaviours are expected. The traditional deliberative design focuses on logical reasoningsearch. In recent years, some soft calculation methods (such as fuzzy logic, neural networks, evolutionary computation, etc.) are applied to the deliberative reasoning design which reflect the comprehensive integration trend. For example, ANN hybrid architecture (Xu Yuru, 2007) used by XU Yu-ru combined with symbolic reasoning and artificial neural

Of course, if you reuse the basic architecture, then the corresponding mapping mechanism can be reused. This gives convenience to system analysis and design. For example, obstacle avoidance and along the wall of the differential behavior robot studied by Joseph (Jones, J.L. 2004), the same type of obstacle avoidance sensors are equipped on robot's left and right sides. He took mapping mechanism as common linear transformation model *A* , provided us with general ideas based on behavior analysis model. Simply select the appropriate

Status signal: 1 indicates that this behavior is triggered, 0 indicates that this behavior is not

In its light-seeking robot design, if *Tr V* , *V* means speed navigation; *R K(El Er )* , *K*

Currently, the concept that intelligent system should be layered has been widely recognized by everyone. The four-model computer systems and the seven-model computer network architecture, fully reflects the level of modeling ideas. Intelligent robot system with multilevel, three-dimensional and flexible, dynamic organizational structure and other features, are the same as cognitive scientists believe that animals have different levels of spatial capacity (reflection, integration, learning and cognitive). Therefore, the simulated biological process of intelligent systems also run through this philosophy. For example, the hierarchical structure of Saridis, level from low to high and intelligent increasing, the control

*T K V*

*K*

Then there exists a common linear transformation matrix *T* .Such that *IB ET* .

for the normalization factor, the corresponding linear transformation matrix is:

network, fully embodied the strengths of complementary of fusion.

transformation matrix *A* , reactive intelligent behavior can be achieved.

*Tr* : Translation speed; *R* : Rotation speed; *Tri* : Trigger flag Assuming the sensor input *El* and *Er* , The matrix is expressed as:

Intelligent behavior IB matrix expressed as:

**4.2.3 Architecture of the spatial distribution**

triggered

precision is decreasing. The subsumption structure of Brooks, with that the high-level tolerant and include the low-level behavior and have the characteristics of stacked layers, this hierarchy has provided a convenient for building robotic system. In this framework, or the distribution of each layer, the component form of organization may be divided into centralized, distributed and hybrid (centralized/ distributed). The intelligence are increasing with the level of compatibility, from low to high. The comparison of three kinds of organization for designing system in Fig.1 are described in Table 3.

Table 3. Comparison of three organized manner based on component

## **4.2.4 The comprehensive integration of structure space**

In the process of function design space are converted into the structure designing space, the functional set is converted into the basic architecture by the primitive. Then the nonfunctional requirements and performance indicators are reflected in the architecture of the constraints. If we use these components to achieve the function of Fr set, the connection between components and the algorithm can reflect the constraints of Ds and NFr set. Thus, according to functional and non functional requirements, we determine the basic structure of the analysis model, and select appropriate mapping mechanism to realize, and achieve a function designing space completely transforms into a subset of structure designing space (basic structure). Then the problems are transformed into that how to use these form the basic structure of the whole system model.

From the view of system theory, complex systems have a Character of "hierarchy" and typically have a structure of modular, so we use the method of hierarchical – aggregation and block-integrate modeling to solve this problem. Nearly 30 years analysis research of robot architecture shows that hybrid architecture is still dominant when the basic model and its organization are identified, we use the comprehensive integrated spatial structure of intelligent service as Fig.1 to build system gradually.

The robotic system building process are descried simply as following after the AD set of structure design space is determined, we can use the method of combination modeling to

Research on Building Mechanism of System for Intelligent Service Mobile Robot 95

designing model includes the selections of computing platform and computational model.

Robot system is an information processing system in essence, whether deliberative plan or the acts reactive of synthesis, ultimately it comes down to a collection of computer-based hardware and software platform. Therefore, at this stage, we can use computer software

The difference between system design and architecture model is that the step of the former faces up with implementation and its main indicators are achievability and operability.We must firstly analyze quantitatively design specifications according to AD space including functional requirements and non-functional requirements from FD. Depending on the selected computer platform, we identified to achieve the basic architecture structure of the software modules (algorithms) and hardware modules (sensors, actuators and computing platforms), scope and interaction protocol between modules. In the design of architecture, when hardware module division has been very clear, we can determine the technology solutions of the hardware, according to the algorithm complexity of basic architecture model and the spatial and temporal distribution of the whole architecture. Next, the solution of the core issue is to choose the appropriate mode of computing, and use the appropriate tools to analyze and design a specific algorithm, so as to achieve comprehensive integrated modelling from different modular granularity. According to this idea, the design process of robotic system is

converted into the next software engineering design problems for specific applications.

To sum up, the robot software design, there are four key application computing models: process-oriented (Process-oriented) approach, object-oriented (Object-oriented) approach for the component (Component-oriented ) methods and agent-oriented (Agent-oriented) approach. In order to select the appropriate computing model, we conduct a comparative

oriented Object-oriented Component-

Basic unit Sub program object component agent

Granularity small smller thicker thick

UML, ROSE, Usecase Diagram etc.

PC, ARM program and ROS design

modular Component

Function call Message,

element Function Object, class,

MCU, DSP program, etc

Table 5. Comparison of computing model

oriented Agent-oriented

response

Include organization, actor, target etc. excepting object and component

AML, AUML, Gaia etc.

Construction of complex intelligent system

response Message, response Message,

COM/DCOM Java Bean CORBA

Design of ditributed system based on WEB

Therefore, the system design space is expressed as a collection of

SD = {computing platform, computing mode}.

engineering approach to modeling and analysis.

analysis from six areas, as shown in Table 5.

Category Process-

Model tools Flow chart

Computer process

Abstrct

Typical applications

integrate *AD { A , A ,A ,...,A }* <sup>123</sup> *<sup>n</sup>* as the spatial structure under the frame in Fig.1. Each *Ai* represents a basic architecture, including components, connectors and constraints, which are used to form the overall system model. In this modelling process, according to the constraints, used the basic modular architecture, based on deliberative and reactive paradigms and different three-dimensional spatial structure of the 5-layer classification method, the association model is established , and then the various subsystems are comprehensive integrated organically. Finally, the overall model are formed. Since the independence of each layer, it enables the level of the surface to be extended independently. Combined with bottomup, coordination ensures the deliberation and reaction layer to perform in parallel, thus, the openness and real-time have greatly improved.

## **4.2.5 Architecture assessment**

The specific evaluation of architecture mainly comes from the constraints of FD. After FD are transformed into AD, these constraints were implicated in the design of architecture. Architecture evaluation requires a certain standard. Kolp(Kolp, 2006), based on research in Akin, derived from four qualitative evaluation criterias : support for modular, well-targeted features, easy to transplant to other areas and robustness. Anders Oreback (Anders, 2003) comprehensively evaluates the architecture of the mobile robot from aspects of hardware abstraction, scalability, uptime, software features, the implementing agency control modeling, tools, methods, documentation and others. Kolp put forward four qualitative evaluation standards from the perspective of organization theory: coherence, predictability, fault tolerance and adaptability, emphasis on evaluation of software systems . Drawing on these assessment criteria, particularly on the base of Anders Oreback, and the architecture design and analysis phase, we propose eight quality attributes to qualitatively assess the architecture design's reasonabilityas shown in Table 4. The architecture trade off analysis method(ATAM) is used to assess the architecture overall.

Table 4. Quality attributes of architecture structure assessment

## **4.3 System implementation**

When the structure of the architecture design space is established, we form the specific system design space (SD), according to the whole comprehensive integration of assessed architecture. On this basis, we choose a suitable computing platform designed to achieve specific functions, and then to form the model for engineering system design. System designing model includes the selections of computing platform and computational model. Therefore, the system design space is expressed as a collection of

SD = {computing platform, computing mode}.

Mobile Robots – Control Architectures,

94 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

integrate *AD { A , A ,A ,...,A }* <sup>123</sup> *<sup>n</sup>* as the spatial structure under the frame in Fig.1. Each *Ai* represents a basic architecture, including components, connectors and constraints, which are used to form the overall system model. In this modelling process, according to the constraints, used the basic modular architecture, based on deliberative and reactive paradigms and different three-dimensional spatial structure of the 5-layer classification method, the association model is established , and then the various subsystems are comprehensive integrated organically. Finally, the overall model are formed. Since the independence of each layer, it enables the level of the surface to be extended independently. Combined with bottomup, coordination ensures the deliberation and reaction layer to perform in parallel, thus, the

The specific evaluation of architecture mainly comes from the constraints of FD. After FD are transformed into AD, these constraints were implicated in the design of architecture. Architecture evaluation requires a certain standard. Kolp(Kolp, 2006), based on research in Akin, derived from four qualitative evaluation criterias : support for modular, well-targeted features, easy to transplant to other areas and robustness. Anders Oreback (Anders, 2003) comprehensively evaluates the architecture of the mobile robot from aspects of hardware abstraction, scalability, uptime, software features, the implementing agency control modeling, tools, methods, documentation and others. Kolp put forward four qualitative evaluation standards from the perspective of organization theory: coherence, predictability, fault tolerance and adaptability, emphasis on evaluation of software systems . Drawing on these assessment criteria, particularly on the base of Anders Oreback, and the architecture design and analysis phase, we propose eight quality attributes to qualitatively assess the architecture design's reasonabilityas shown in Table 4. The architecture trade off analysis

openness and real-time have greatly improved.

method(ATAM) is used to assess the architecture overall.

No Quality attributes Describe

Table 4. Quality attributes of architecture structure assessment

1 Niche targetability Functional integrity of the design space and

5 robustness Anti-interference ability and measures

7 Security Meet the three principles of Asimov 8 Document Well detailed decumants

2 Modularity Support modular, tight cohesion and loose coupling 3 Openess Scalability, portability and reconfigurability

capacity Real-time response

When the structure of the architecture design space is established, we form the specific system design space (SD), according to the whole comprehensive integration of assessed architecture. On this basis, we choose a suitable computing platform designed to achieve specific functions, and then to form the model for engineering system design. System

environment Have adapt to the unkown and dynamic environment

consistency to architecture mapping

**4.2.5 Architecture assessment**

<sup>4</sup>Adapt to the

<sup>6</sup>Real-time response

**4.3 System implementation**

Robot system is an information processing system in essence, whether deliberative plan or the acts reactive of synthesis, ultimately it comes down to a collection of computer-based hardware and software platform. Therefore, at this stage, we can use computer software engineering approach to modeling and analysis.

The difference between system design and architecture model is that the step of the former faces up with implementation and its main indicators are achievability and operability.We must firstly analyze quantitatively design specifications according to AD space including functional requirements and non-functional requirements from FD. Depending on the selected computer platform, we identified to achieve the basic architecture structure of the software modules (algorithms) and hardware modules (sensors, actuators and computing platforms), scope and interaction protocol between modules. In the design of architecture, when hardware module division has been very clear, we can determine the technology solutions of the hardware, according to the algorithm complexity of basic architecture model and the spatial and temporal distribution of the whole architecture. Next, the solution of the core issue is to choose the appropriate mode of computing, and use the appropriate tools to analyze and design a specific algorithm, so as to achieve comprehensive integrated modelling from different modular granularity. According to this idea, the design process of robotic system is converted into the next software engineering design problems for specific applications.

To sum up, the robot software design, there are four key application computing models: process-oriented (Process-oriented) approach, object-oriented (Object-oriented) approach for the component (Component-oriented ) methods and agent-oriented (Agent-oriented) approach. In order to select the appropriate computing model, we conduct a comparative analysis from six areas, as shown in Table 5.

Research on Building Mechanism of System for Intelligent Service Mobile Robot 97

*(x) x A*

Set A is called the characteristic function. According to the nature of the characteristic function, the development model can be represented by equation (8) to. For any *<sup>i</sup> x FD* ,

> *(x ) (y ) (z ) (k ) (z )*

Let us take a specific example to illustrate theory above. The intelligent gas-check service mobile robot (IGSMR) is used to check toxic gas autonomously through roaming or default route in chemical industry zone. And it can cooperate with human and help people detect gas leakage. Under the open and space-oriented reference architecture of ISMR with learning mechanism, according to the development model of ISMR based on architecture, the building process of IGSMR is divided into the design of FD, AD, SD and SE. And the

2. Design the basic aechitecture analysis model accoring to FD set, and then integrate comprehensively the architecture space, output system reference architecture and AD set. 3. Select approprite computing model according AD set and Table 3, and then form system design model, finally output SD set and cooresponding physical

4. Evolve (interative incremental correction) in the light of Fig.3 and test system, if it does not meet the requirements, return 1), otherwise, extract common components to SE.

*x A*

*f* , *'*

(7)

*f : FD AD SD* , satisfies the

(8)

0 *<sup>A</sup>*

*SE j SD i*

1. Design functional space according to requirement analysis and ouput FD set.

5. Output detailed and standard documents after finishing the first four steps.

Based on the U of A is a subset of the function:

design steps can be described as following.

implementation model.

Fig. 4. Mulan No.1

equation:

**5. Case study**

<sup>1</sup>

*<sup>i</sup> y AD* , *<sup>i</sup> z SD* and, *<sup>i</sup> k SE* , *i , , ,...,n* 123 , present *'*

<sup>1</sup> *FD i AD i SD i*

On the view of the application of the robot engineering, these four computing modes are coexistence in the system design. We select the appropriate calculation patterns according to the algorithm, organizational structure identified in the system model and spatial distribution of the system. Then design the sub-module and the sub-system and determine the software development environment, following the corresponding modeling tools. So it is easy to build robot system software architecture. In addition, when we have completed the transition from system architecture space mode to system design space mode, we can test and evolve the algorithm of structure designing space through the simulation space or combination of hardware. modify the architecture design model by iterative incremental ways, until it meets system-related design specifications, finially establish a table or graph form the detailed system design documentation.

## **4.4 System evolution**

Robot systems analysis and design is a dynamic iterative process. In the life cycle of systems analysis and design, the need of users and application areas of the environment may all change. Therefore, in architecture design, we need to consider the openness and scalability of architecture, in order to create conditions for the evolution of the system. Following successful tests, the system will extract some of the common framework to form the evolution of the design space (SE). If the system needs change, we must first update the functional design space, then search the evolution space (SE) that if there are ready component, and select add or reuse an existing structure, to achieve this function to find the basic architecture, in order to build a system design model. Accordingly, if the system tests have problems, we can date back to FD design space via the elements of SD design space. In the systems analysis and design of the architecture-based design and development model, system evolution is divided into two main lines. The first one is done to the structure design space from the functional design space, and done to system design space from the design space. Another is the change in demand, the use of the basic architecture and components extracted from homogeneous to update (add or delete) AD and SD elements. The dynamic evolution of the model shown in Fig. 3, this figure showing the relationship between the four design space.

When we decompose the robot system development process model into FD, AD, SD, SE four design space, we can use the collection analysis methods of robotic systems to get the qualitative and quantitative information. Here, we introduce a collection of characteristic functions, in order to complete the conversion of the four design space and structural modeling.

Fig. 3. Models the dynamic evolution of the system

Based on the U of A is a subset of the function:

$$\mu\_A(\mathbf{x}) = \begin{cases} 1 & \mathbf{x} \in A \\ 0 & \mathbf{x} \notin A \end{cases} \tag{7}$$

Set A is called the characteristic function. According to the nature of the characteristic function, the development model can be represented by equation (8) to. For any *<sup>i</sup> x FD* , *<sup>i</sup> y AD* , *<sup>i</sup> z SD* and, *<sup>i</sup> k SE* , *i , , ,...,n* 123 , present *' f* , *' f : FD AD SD* , satisfies the equation:

$$\begin{cases} \mu\_{\rm FD}(\mathbf{x}\_i)\mu\_{\rm AD}(y\_i)\mu\_{\rm SD}(z\_i) = 1\\ \mu\_{\rm SE}(k\_j) \le \mu\_{\rm SD}(z\_i) \end{cases} \tag{8}$$

## **5. Case study**

Mobile Robots – Control Architectures,

96 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

On the view of the application of the robot engineering, these four computing modes are coexistence in the system design. We select the appropriate calculation patterns according to the algorithm, organizational structure identified in the system model and spatial distribution of the system. Then design the sub-module and the sub-system and determine the software development environment, following the corresponding modeling tools. So it is easy to build robot system software architecture. In addition, when we have completed the transition from system architecture space mode to system design space mode, we can test and evolve the algorithm of structure designing space through the simulation space or combination of hardware. modify the architecture design model by iterative incremental ways, until it meets system-related design specifications, finially establish a table or graph

Robot systems analysis and design is a dynamic iterative process. In the life cycle of systems analysis and design, the need of users and application areas of the environment may all change. Therefore, in architecture design, we need to consider the openness and scalability of architecture, in order to create conditions for the evolution of the system. Following successful tests, the system will extract some of the common framework to form the evolution of the design space (SE). If the system needs change, we must first update the functional design space, then search the evolution space (SE) that if there are ready component, and select add or reuse an existing structure, to achieve this function to find the basic architecture, in order to build a system design model. Accordingly, if the system tests have problems, we can date back to FD design space via the elements of SD design space. In the systems analysis and design of the architecture-based design and development model, system evolution is divided into two main lines. The first one is done to the structure design space from the functional design space, and done to system design space from the design space. Another is the change in demand, the use of the basic architecture and components extracted from homogeneous to update (add or delete) AD and SD elements. The dynamic evolution of the model shown in Fig. 3, this figure

When we decompose the robot system development process model into FD, AD, SD, SE four design space, we can use the collection analysis methods of robotic systems to get the qualitative and quantitative information. Here, we introduce a collection of characteristic functions, in order to complete the conversion of the four design space and structural

FD AD SD

maping

component update

Extract

Prototype

Track Track

demand SE

form the detailed system design documentation.

showing the relationship between the four design space.

maping

Fig. 3. Models the dynamic evolution of the system

Architecture update

**4.4 System evolution**

modeling.

Changes in

Let us take a specific example to illustrate theory above. The intelligent gas-check service mobile robot (IGSMR) is used to check toxic gas autonomously through roaming or default route in chemical industry zone. And it can cooperate with human and help people detect gas leakage. Under the open and space-oriented reference architecture of ISMR with learning mechanism, according to the development model of ISMR based on architecture, the building process of IGSMR is divided into the design of FD, AD, SD and SE. And the design steps can be described as following.

Fig. 4. Mulan No.1

Research on Building Mechanism of System for Intelligent Service Mobile Robot 99

In this article, we have presented a new building mechanism of system for intelligent service mobile robot employed to address the robot system analysis and design problems. On the basis of previous studies, we provide firstly an open and space-oriented reference architecture of ISMR with learning mechanism, and then learning from software research methods, we present the development model of intelligent service robot system based on architecture which provide some engineering principles and theory foundation for building

The design processes of ISMR are divided into four dynamic design spaces (FD, AD, SD and SE) which are contributed to integrate comprehensively. In addition, the three-dimensional spatial structure and how to design it, and formal theoretical model based on set analysis is provided. Compared to methods of conceptual design and integration (Yavuz, 2007), the methods in this paper have some obvious merits including intuitive, level clear, detailed size classification and easy computer-aided analysis, etc. From the case study of IGSMR, we conclude that this strategy not only have clear organization of intelligence, but also have high scalability and efficiency compared with traditional design methods. During the process of developing IGSMR, we use this method based on architecture to make overall efficiency increased by 40%. Moreover, the methods of analysis and design for ISMR have provided a new thought for architecture optimization and reuse of intelligent robotic

The authors wish to thank the helpful comments and suggestions from our teachers and colleagues in intelligent detection and control lab of HIT at Weihai. And thank the Shanghai Chemical industry park public pipe gallery Co.,Ltd.This work is supported by the study

Albus.J.S, McCain, R. Lumia(1989), NASA/NBS standard reference model for telerobot

Anders Orebäck and Henrik I.Christensen(2003). Evaluation of architecture for mobile

Bogdan.R, Radu.R, Robotin, et al. (2006). Towards open architecture for mobile robots :Zee

Cao longbin, Dai Ruwei (2008), *Fundamentals, conceptss, analysis and design and*

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control system architecture. *National instutute of standards and technology,* technical

Ro, *Proceedings of IEEE international conference on automation, quality and testing,*

fund of HIT at Weihai (No.IMVQ02020003 and IMJQ21080002).

robotics. *Autonomous robots*, Vol.13, pp.33-49

*implementation,* Post and Telecom press, China

**6. Conclusion**

robot system effectively.

**7. Acknowledgment**

**8. References**

Report.

*robotics,* pp. 260-265.

system.

## Fig. 5. Mulan No.2

Table 6. Architecture design space of IGSMR

From this design process, we can see that the design steps above are easy realized by computer. Because of space restrictions of this article, we manily focus on the architecture design of space. The architecture design is the core of buidling IGSMR, and the core of basic architecture is the mapping mechanism. If we grasp the core mechanism, we build robot ic system easily according to Fig.1. The physicals of IGSMR with navigation(Mulan No.1) and own roaming (Mulan No.2) are shown such as Fig.4 and Fig.5.

## **6. Conclusion**

Mobile Robots – Control Architectures,

Mapping Mechanism(f)

Generalized linear transformation

Fuzzy logic and Reinforcement learning

Generalized linear transformation

transformation

leaining on-line

Generalized linear transformation

Status indication linear

Alarm or preprocess

98 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

Environment sensor(E)

A2 Reactive Infrared Obstacle avoidance

A4 Reactive Ultrasound Own roaming

A5 Deliberative Network signal Runing state control

State detection within environment

sensors

A1 Reactive Code/decode Differential drive PID

A3 Deliberative Magnetic sensor Navigation Fuzzy logic

A6 Deliberative Power check Independent charge Fuzzy logic

A8 Deliberative Sound Sound identification Fuzzy logic and

A9 Deliberative Photosensitive Image processing Soft computing

From this design process, we can see that the design steps above are easy realized by computer. Because of space restrictions of this article, we manily focus on the architecture design of space. The architecture design is the core of buidling IGSMR, and the core of basic architecture is the mapping mechanism. If we grasp the core mechanism, we build robot ic system easily according to Fig.1. The physicals of IGSMR with navigation(Mulan No.1) and

Intelligent Behaviors(IB)

Fig. 5. Mulan No.2

Subset of AD Basic

A7 Reactive

A10 Deliberative Gas-check

own roaming (Mulan No.2) are shown such as Fig.4 and Fig.5.

Table 6. Architecture design space of IGSMR

architecture

In this article, we have presented a new building mechanism of system for intelligent service mobile robot employed to address the robot system analysis and design problems. On the basis of previous studies, we provide firstly an open and space-oriented reference architecture of ISMR with learning mechanism, and then learning from software research methods, we present the development model of intelligent service robot system based on architecture which provide some engineering principles and theory foundation for building robot system effectively.

The design processes of ISMR are divided into four dynamic design spaces (FD, AD, SD and SE) which are contributed to integrate comprehensively. In addition, the three-dimensional spatial structure and how to design it, and formal theoretical model based on set analysis is provided. Compared to methods of conceptual design and integration (Yavuz, 2007), the methods in this paper have some obvious merits including intuitive, level clear, detailed size classification and easy computer-aided analysis, etc. From the case study of IGSMR, we conclude that this strategy not only have clear organization of intelligence, but also have high scalability and efficiency compared with traditional design methods. During the process of developing IGSMR, we use this method based on architecture to make overall efficiency increased by 40%. Moreover, the methods of analysis and design for ISMR have provided a new thought for architecture optimization and reuse of intelligent robotic system.

## **7. Acknowledgment**

The authors wish to thank the helpful comments and suggestions from our teachers and colleagues in intelligent detection and control lab of HIT at Weihai. And thank the Shanghai Chemical industry park public pipe gallery Co.,Ltd.This work is supported by the study fund of HIT at Weihai (No.IMVQ02020003 and IMJQ21080002).

## **8. References**

**5**

*Malaysia*

**A Robotic Wheelchair Component-Based**

A robotic wheelchair system provides mobility for handicapped and elderly people who are unable to operate classical wheelchair system. Software development for such system is challenged by requirement for multi-disciplines expert knowledge which includes embedded systems, real-time software issues, control theories and artificial intelligence aspects. Software reuse is an approach to provide a way to reuse expertise that can be used across domains in software engineering. Software reuse can be a mechanism to support the attempts to transfer technology from other engineering fields to rehabilitation engineering. For example, (Bonail et al., 2009) and (Cheein et al., 2009) have attempted to transfer software platform and algorithms from robotic technologies to rehabilitation engineering software development. The technologies transfer requires a methodological support to

Software reuse is one of the promising approaches to increase software productivity and improve its quality, as well as to decrease the costs of software development. This is because of software reuse uses existing software either in the form of component or knowledge to construct new software. Yet, applying software reuse in Embedded Real-Time (ERT) domain, such as robotic wheelchair sets major challenges to the software development

In order to overcome multi-constraints and multi-disciplinary knowledge in ERT software development problems, Component-Based Development (CBD) method becomes a promising approach for ERT software development (Bunse & Gross, 2006; Carlson et al., 2006). Existing industrial component technologies such as OMG's CORBA Component Model (CCM), Microsoft's (D) COM/COM++, .NET, SUN Microsystems' JavaBeans and enterprise JavaBeans, are not suitable to develop ERT systems because they do not address

With the purpose to meet the requirements of ERT systems, a number of component technologies such as Koala (Ommering, 2000), PECOS (Nierstrasz, et al., 2002) and KobrA (Atkinson, et al., 2002) have emerged. However, these component technologies still have some weaknesses. Koala and PECOS cannot support multi-disciplinary knowledge, but they can

enable a systematic software reuse of the multi-disciplines expert knowledge.

process due to the resource-constrained and real-time requirements of the system.

Suzila Sabil, Rosbi Mamat, Mohd Zulkifli Mohd Zaki, Mahmood Aghajani Siroos Talab,

the non-functional properties in ERT systems.

*Universiti Teknologi Malaysia, Malaysia*

Radziah Mohamad, Norazian M. Hamdan and Khadijah Kamal

 \*

**1. Introduction**

**Software Development**

Dayang N. A. Jawawi et al.\* *Universiti Teknologi Malaysia*

Manuel Kolp, P.G.J.M (2006), Muti-agent architecture as organizational structures. *Auton Agent Multi-Agent sys,* Vol.13, No. 3, pp. 3-25

## **A Robotic Wheelchair Component-Based Software Development**

Dayang N. A. Jawawi et al.\* *Universiti Teknologi Malaysia Malaysia*

## **1. Introduction**

Mobile Robots – Control Architectures,

100 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

Grelle.C, et al. (2006) Agent-based architecture for designing hybrid control systems.

Gu.J.S, C.W .de Silva(2004). Development and implementation of a real-time open-

Hejian, Jia xiaolin, et.al (2004). Model of software analysis and design process based on architecture. *Journal of Xi,an Jiaotong university*, Vol.38. No.6, pp.591-594. Inniceni.B, et al.(2007) A multi-agent architecture with cooperative fuzzy control for a mobile robot. *Robotics and autonomous system,* Vol.55, No.12, pp. 881-891 Jones, J.L(2004), *Robot programing –A-practical guide to behavior-based Robotics,* McGraw-Hill

Manuel Kolp, P.G.J.M (2006), Muti-agent architecture as organizational structures. *Auton*

Nehmzow.U. (2006).*, Scientific methods in mobile robotics-quantitative analysis of agent*

Pfeifer.R, Fumiya Iida, Josh Bongard (2005). New robotics : Design principles for intelligent

Posadas, J.L.. et al.(2008) Agent-based distributed architecture for mobile robot control *Engineering applications of artificial intelligence,* Vol.21, No. 6, pp. 805-823 Slusn, S., R.Neruda and P.Vidnerov(2010) Comparison of behaviour-based and planning

Tan zhen, hejian, et al.(2002). *Software architecture,* Xi'an Jiaotong university press, China Xie Wei, Ma jiachen, Yang mingli (2010). Agent-oriented architecture for intelligent service

Xie Wei, Ma jiachen, Yang mingli (2010). Design of hybrid architecture for intelligent service

Xu Yu-ru, Xiao kun(2007). Technology development of autonomous ocean vehicle. *Acta*

Yavuz.H (2002). A new conceptual approach to the design of hybrid control architecture for autonomous mobile robots. *Journal of intelligent and robotic system,* Vol.34, pp. 1-26 Yavuz.H (2007). An integrated approach the conceptual design and development of an

Zhang yousheng(2004). Architecture-based software development model. *Compute*

Zhu Miaoliang (2001), *Autonomous intelligent system*, Zhejiang university press, China

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mobile robot. *Proceedings of IEEE international conference on electrical and control*

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*Information science,* Vol.176, No. 9, pp. 1103-1130

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*behaviour*, Spring-Verlag london

Companies.inc

No. 4, pp. 560-567

*engineering,* pp. 740-743

5964-5967

498-512

A robotic wheelchair system provides mobility for handicapped and elderly people who are unable to operate classical wheelchair system. Software development for such system is challenged by requirement for multi-disciplines expert knowledge which includes embedded systems, real-time software issues, control theories and artificial intelligence aspects. Software reuse is an approach to provide a way to reuse expertise that can be used across domains in software engineering. Software reuse can be a mechanism to support the attempts to transfer technology from other engineering fields to rehabilitation engineering. For example, (Bonail et al., 2009) and (Cheein et al., 2009) have attempted to transfer software platform and algorithms from robotic technologies to rehabilitation engineering software development. The technologies transfer requires a methodological support to enable a systematic software reuse of the multi-disciplines expert knowledge.

Software reuse is one of the promising approaches to increase software productivity and improve its quality, as well as to decrease the costs of software development. This is because of software reuse uses existing software either in the form of component or knowledge to construct new software. Yet, applying software reuse in Embedded Real-Time (ERT) domain, such as robotic wheelchair sets major challenges to the software development process due to the resource-constrained and real-time requirements of the system.

In order to overcome multi-constraints and multi-disciplinary knowledge in ERT software development problems, Component-Based Development (CBD) method becomes a promising approach for ERT software development (Bunse & Gross, 2006; Carlson et al., 2006). Existing industrial component technologies such as OMG's CORBA Component Model (CCM), Microsoft's (D) COM/COM++, .NET, SUN Microsystems' JavaBeans and enterprise JavaBeans, are not suitable to develop ERT systems because they do not address the non-functional properties in ERT systems.

With the purpose to meet the requirements of ERT systems, a number of component technologies such as Koala (Ommering, 2000), PECOS (Nierstrasz, et al., 2002) and KobrA (Atkinson, et al., 2002) have emerged. However, these component technologies still have some weaknesses. Koala and PECOS cannot support multi-disciplinary knowledge, but they can

Radziah Mohamad, Norazian M. Hamdan and Khadijah Kamal

<sup>\*</sup> Suzila Sabil, Rosbi Mamat, Mohd Zulkifli Mohd Zaki, Mahmood Aghajani Siroos Talab,

*Universiti Teknologi Malaysia, Malaysia*

A Robotic Wheelchair Component-Based Software Development 103

A prototype of robotic wheelchair was developed to support our researches in ERT software engineering and rehabilitation robotics. The main considerations when developing this prototype were to have a low cost and 'open' system such that it enables different aspects of

Due to these considerations, rather than basing the prototype on a standard power wheelchair such as the smart wheelchair system developed by Simpson et al. (2004), a commercially available manual wheelchair was used as the base for the robotic wheelchair prototype. An easily detachable add-on unit was developed to be attached to the manual wheelchair without significant modifications to the manual wheelchair. This add on unit consists of two geared direct current (DC) motors, two 12V batteries and a control box with embedded processor and associated electronics. Fig. 1 shows the prototype of robotic wheelchair which consists of a standard manual wheelchair and add on unit. The DC motors together with two small wheels provide the motorized wheels for mobility. The DC motors are powered by the 24V 14 Ampere obtained from the two 12V batteries. The voltage regulators in the control box provide 5V supplies for the sensors and the embedded

Fig. 1. The prototype of robotic wheelchair. The motorized wheels are under the add on unit To provide the capability for sensing the environment, sets of sensors are attached to the robotic wheelchair. Infra red (IR) distance sensors are used to detect obstacles in the direction of the robotic wheelchair movement. Four IR sensors are mounted at the front and two IR sensors are mounted at the back. Sonar sensor is used to map room environment or

Currently, the robotic wheelchair supports two ways of controlling. The first way is through the usual joystick or keypad control. The second way is through the movements of head. Head movements control is particularly useful for severely-handicapped people who have spinal cord injury or quadriplegia which cannot use their hands to control the wheelchair. In the developed prototype, the head movement control of the robotic wheelchair is achieved with the help of accelerometer or tilt sensor. The accelerometer senses head movements and based

detect far obstacles. It is mounted on the pole above the user head.

**2. The robotic wheelchair system**

processor from the two batteries.

hardware and software experimentations to be performed.

support different multi-constraint extra-functionality requirement. On the contrary, Koala supports resource constraint and PECOS supports timing problems. KobrA does not support ERT system development, but it has an extension that calls MARMOT (Bunse & Gross, 2006) to support multi-disciplinary knowledge in ERT system development. The only limitation of MARMOT is minimum support for multi-constraint extra-functionality requirement.

Therefore, PECOS is suitable to support multi-constraint in extra-functionality requirements whereby, MARMOT is good to support multi-disciplinary knowledge. An integration of PECOS and MARMOT can be a promising strategy to support the two issues. Implying a methodological support to enable a systematic CBD can be an important approach with consideration of the two issues. Currently, there are many Object-Oriented Analysis and Design (OOAD) methodology available, but for Component-Oriented Analysis and Design (COAD) method, the focus is still on PC based domain such as the web application (Lee and Shirani, 2004) and simulation systems (Gong et al., 2010). Component technologies also improved along with engineering practices, but they lack of a methodology that uses components within such a paradigm (Dogru & Tanik, 2003).

Motivated by these challenges, the focus of this chapter is to propose a method for developing robotic wheelchair software using a set of reusable software components obtained from mobile robot software. The method was adapted from general ERT component technologies and was applied to a robotic wheelchair CBD. The method is a combination of MARMOT and PECOS technologies aiming to support CBD methodological with purpose to solve multi-constraint extra-functionality requirement and multidisciplinary knowledge that are required in the robotic wheelchair software development. The proposed systematic CBD process model is based on the Component-Based Software Engineering (CBSE) that is defined by Wang and Qian (2005). CBSE is a combination of Component-Oriented Analysis (COA), Component-Oriented Design (COD) and Component-Oriented Programming (COP) and Component-Oriented Management (COM). This chapter focuses on COA, COD and COP process of development, and this chapter defines the COA, COD and COP modelling and deployment activities of the method in a form of process model representation. The process model depicts understandable integration between MARMOT and PECOS.

This chapter documented the applicability of the process model in a wheelchair software development and implementation. This implementation showed how the process model helped the wheelchair hardware and software engineer to identify the possible software reused component in the early stage of the system and software development. The amount of software component reused in the wheelchair CBD from a mobile robot system was discussed and compared with a reused case from a mobile robot to another mobile robot CBD. The objective of the comparison was to identify the differences between the software reuse to support technologies transfer from robotic technologies to rehabilitation engineering with software reuse within robotics systems. Software reuse in robotics domain is one of the focuses area in current robotic research, example of the study are by Nesnas et al. (2006), Jang et al (2010) and Mallet et al. (2007).

The layout of this chapter is as follows: Section 2 discusses the robotic wheelchair design considerations and the hardware description. In section 3 the strategy to define the process model to support the CBD of the wheelchair software are described. The process model to support the CBD method is described in Section 4. Section 5 illustrates the process model in a CBD of the robotic wheelchair software. The comparison result to compare the process model with other models will be discussed in detail in Section 6. The Section 7 concludes the chapter.

## **2. The robotic wheelchair system**

Mobile Robots – Control Architectures,

102 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

support different multi-constraint extra-functionality requirement. On the contrary, Koala supports resource constraint and PECOS supports timing problems. KobrA does not support ERT system development, but it has an extension that calls MARMOT (Bunse & Gross, 2006) to support multi-disciplinary knowledge in ERT system development. The only limitation of

Therefore, PECOS is suitable to support multi-constraint in extra-functionality requirements whereby, MARMOT is good to support multi-disciplinary knowledge. An integration of PECOS and MARMOT can be a promising strategy to support the two issues. Implying a methodological support to enable a systematic CBD can be an important approach with consideration of the two issues. Currently, there are many Object-Oriented Analysis and Design (OOAD) methodology available, but for Component-Oriented Analysis and Design (COAD) method, the focus is still on PC based domain such as the web application (Lee and Shirani, 2004) and simulation systems (Gong et al., 2010). Component technologies also improved along with engineering practices, but they lack of a methodology that uses

Motivated by these challenges, the focus of this chapter is to propose a method for developing robotic wheelchair software using a set of reusable software components obtained from mobile robot software. The method was adapted from general ERT component technologies and was applied to a robotic wheelchair CBD. The method is a combination of MARMOT and PECOS technologies aiming to support CBD methodological with purpose to solve multi-constraint extra-functionality requirement and multidisciplinary knowledge that are required in the robotic wheelchair software development. The proposed systematic CBD process model is based on the Component-Based Software Engineering (CBSE) that is defined by Wang and Qian (2005). CBSE is a combination of Component-Oriented Analysis (COA), Component-Oriented Design (COD) and Component-Oriented Programming (COP) and Component-Oriented Management (COM). This chapter focuses on COA, COD and COP process of development, and this chapter defines the COA, COD and COP modelling and deployment activities of the method in a form of process model representation. The process model depicts understandable

This chapter documented the applicability of the process model in a wheelchair software development and implementation. This implementation showed how the process model helped the wheelchair hardware and software engineer to identify the possible software reused component in the early stage of the system and software development. The amount of software component reused in the wheelchair CBD from a mobile robot system was discussed and compared with a reused case from a mobile robot to another mobile robot CBD. The objective of the comparison was to identify the differences between the software reuse to support technologies transfer from robotic technologies to rehabilitation engineering with software reuse within robotics systems. Software reuse in robotics domain is one of the focuses area in current robotic research, example of the study are by Nesnas et

The layout of this chapter is as follows: Section 2 discusses the robotic wheelchair design considerations and the hardware description. In section 3 the strategy to define the process model to support the CBD of the wheelchair software are described. The process model to support the CBD method is described in Section 4. Section 5 illustrates the process model in a CBD of the robotic wheelchair software. The comparison result to compare the process model with other models will be discussed in detail in Section 6. The Section 7 concludes the chapter.

MARMOT is minimum support for multi-constraint extra-functionality requirement.

components within such a paradigm (Dogru & Tanik, 2003).

integration between MARMOT and PECOS.

al. (2006), Jang et al (2010) and Mallet et al. (2007).

A prototype of robotic wheelchair was developed to support our researches in ERT software engineering and rehabilitation robotics. The main considerations when developing this prototype were to have a low cost and 'open' system such that it enables different aspects of hardware and software experimentations to be performed.

Due to these considerations, rather than basing the prototype on a standard power wheelchair such as the smart wheelchair system developed by Simpson et al. (2004), a commercially available manual wheelchair was used as the base for the robotic wheelchair prototype. An easily detachable add-on unit was developed to be attached to the manual wheelchair without significant modifications to the manual wheelchair. This add on unit consists of two geared direct current (DC) motors, two 12V batteries and a control box with embedded processor and associated electronics. Fig. 1 shows the prototype of robotic wheelchair which consists of a standard manual wheelchair and add on unit. The DC motors together with two small wheels provide the motorized wheels for mobility. The DC motors are powered by the 24V 14 Ampere obtained from the two 12V batteries. The voltage regulators in the control box provide 5V supplies for the sensors and the embedded processor from the two batteries.

Fig. 1. The prototype of robotic wheelchair. The motorized wheels are under the add on unit

To provide the capability for sensing the environment, sets of sensors are attached to the robotic wheelchair. Infra red (IR) distance sensors are used to detect obstacles in the direction of the robotic wheelchair movement. Four IR sensors are mounted at the front and two IR sensors are mounted at the back. Sonar sensor is used to map room environment or detect far obstacles. It is mounted on the pole above the user head.

Currently, the robotic wheelchair supports two ways of controlling. The first way is through the usual joystick or keypad control. The second way is through the movements of head. Head movements control is particularly useful for severely-handicapped people who have spinal cord injury or quadriplegia which cannot use their hands to control the wheelchair. In the developed prototype, the head movement control of the robotic wheelchair is achieved with the help of accelerometer or tilt sensor. The accelerometer senses head movements and based

A Robotic Wheelchair Component-Based Software Development 105

A strategy is needed to allow the mapping of MARMOT and PECOS methods in order to identify overlapping phases in between the two methods. The mapping of these two methods is required in order to propose a new component-oriented developed method based on the two selected component models. Fig. 4 shows the models and phases of the methods. It comprises of three development process which are COA, COD and COP. The development process is then divided into five phases, which are analysis, early design, detailed design, composition and implementation. COA level involves the analysis phase; COD level involves two phases, which are early design and detailed design; and COP level

The analysis phase includes two strategies, which are preliminary information and system architecture planning. Preliminary information consists of the basic requirements of the use case diagram, use case description and interaction model. The system architecture planning transforms the circuit into Unified Modelling Language (UML) representation and in turn

The early design phase contains the finer-grain component of software that further divides into two parts, which are specification and realization. In specification of components, three models are produced, which are functional model, behavioural model, and structural model. The structural model is also produced in realization of components, as well as the

**LEVEL CBD PROCESS MODEL DIAGRAM CM**

Specification - Functional Model - Behavioural Model - Structural Model

Preliminary Information

System Architecture Planning - Hardware UML representation - Preliminary Containment Hierarchy

Finer-Grain Component

Details Information Regular Containment Hierarchy

> Integration Process Composition Diagram

> > Generation Code Code Template

Realization - Structural Model - Activity Model - Interaction Model

MARMOT Method

Integration Point

PECOS Component Model

**3. The strategy to define the component-based development phases**

involves composition and implementation phase.

activity model and interaction model.

Early Design Phase

Detailed Design Phase

Composition Phase

Implementation Phase

Fig. 4. Models and phases of the method

COA Analysis Phase

COD

COP

produces a preliminary containment hierarchy of the whole system.

on the predetermined direction of movements, the robotic wheelchair can be controlled accordingly. Fig. 2 shows the side view of the robotic wheelchair with the locations of sensors.

Fig. 2. The side view of robotic wheelchair prototype with sensors locations.

The embedded processor provides the intelligent decision making and motor control. All the sensors outputs are processed by the embedded processor and control the two DC motors as desired by the user. Automatic reactions such as stop or avoid when obstacles are detected are also handled by the embedded processor.

In the robotic wheelchair prototype an ATMEL ATMega32 8-bit single-chip microcontroller is used as the embedded processor. The ATMega32 has 32 Kbytes of flash program memory, and 2 Kbytes internal Random Access Memory (RAM). The ATMega32 also includes an 8 channels 10-bit analogue-to-digital converter (ADC), three timers, parallel input-output ports and several serial communication interfaces including Serial Peripheral Interface (SPI), Inter-Integrated Circuit (I2C) and Universal Synchronous Asynchronous Receiver Transmitter (USART). Fig. 3 shows the interfaces between the embedded processor, sensors and actuators in the robotic wheelchair.

Fig. 3. The sensors and actuators in the robotic wheelchair.

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104 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

on the predetermined direction of movements, the robotic wheelchair can be controlled accordingly. Fig. 2 shows the side view of the robotic wheelchair with the locations of sensors.

Fig. 2. The side view of robotic wheelchair prototype with sensors locations.

detected are also handled by the embedded processor.

Fig. 3. The sensors and actuators in the robotic wheelchair.

and actuators in the robotic wheelchair.

The embedded processor provides the intelligent decision making and motor control. All the sensors outputs are processed by the embedded processor and control the two DC motors as desired by the user. Automatic reactions such as stop or avoid when obstacles are

In the robotic wheelchair prototype an ATMEL ATMega32 8-bit single-chip microcontroller is used as the embedded processor. The ATMega32 has 32 Kbytes of flash program memory, and 2 Kbytes internal Random Access Memory (RAM). The ATMega32 also includes an 8 channels 10-bit analogue-to-digital converter (ADC), three timers, parallel input-output ports and several serial communication interfaces including Serial Peripheral Interface (SPI), Inter-Integrated Circuit (I2C) and Universal Synchronous Asynchronous Receiver Transmitter (USART). Fig. 3 shows the interfaces between the embedded processor, sensors

## **3. The strategy to define the component-based development phases**

A strategy is needed to allow the mapping of MARMOT and PECOS methods in order to identify overlapping phases in between the two methods. The mapping of these two methods is required in order to propose a new component-oriented developed method based on the two selected component models. Fig. 4 shows the models and phases of the methods. It comprises of three development process which are COA, COD and COP. The development process is then divided into five phases, which are analysis, early design, detailed design, composition and implementation. COA level involves the analysis phase; COD level involves two phases, which are early design and detailed design; and COP level involves composition and implementation phase.

The analysis phase includes two strategies, which are preliminary information and system architecture planning. Preliminary information consists of the basic requirements of the use case diagram, use case description and interaction model. The system architecture planning transforms the circuit into Unified Modelling Language (UML) representation and in turn produces a preliminary containment hierarchy of the whole system.

The early design phase contains the finer-grain component of software that further divides into two parts, which are specification and realization. In specification of components, three models are produced, which are functional model, behavioural model, and structural model. The structural model is also produced in realization of components, as well as the activity model and interaction model.

Fig. 4. Models and phases of the method

A Robotic Wheelchair Component-Based Software Development 107

Fig. 5. SPEM Icons

Fig. 6. Process model phases for the method

Fig. 7. Use case diagram for analysis phase

In order to elaborate upon the preliminary hierarchy containment based on the finer-grain component, regular containment hierarchy is produced at the detailed design phase. Thus at implementation phase, the integration process is performed, and it is represented by the component composition diagram. Based on the component composition diagram, a code template is generated, which includes single component code template and component composition code template.

The models and phases in this research are derived from MARMOT and PECOS models to create a new method; the MARMOT method is applied at analysis, early design and detailed design phase while PECOS is applied at detailed design, composition and implementation phase. The integration point between MARMOT method and PECOS component model is at the detailed design phase, as shown in Fig. 4.

The central notion in COA and COD is the component. COP supports constructing software systems by composing independent components into software architecture. Software component is grouped with its component infrastructure. Component infrastructures include three elements, which are component model, component connection and component deployment. The analysis phase supports COA method, whereas early design and detailed design phases support COD. COP method is supported by two phases, which are composition and implementation phase. The next section provides detailed discussion on the process model of the integrated MARMOT and PECOS method. The process model clarifies the integration point of the two methods.

## **4. A component-based development process model for embedded real time software**

Defining the integrated process of MARMOT and PECOS methods in a systematic form is important to enable and support the development of CASE tool for ERT system. Here, the process is represented using Software Process Engineering Meta-model (SPEM) (Schuppenies & Steinhauer, 2002). SPEM is a meta-model that is used to describe a concrete software development process or family of related software development process. The Fig. 5 below shows the SPEM icons that are used in this project:

There are five phases in the process model; analysis phase, early design phase, detail design phase, composition phase and implementation phase. Each of these phases produces a work product for the related activities and has its own responsibilities in designing one or more artefacts. Fig. 6 illustrates the phases in COA, COD and COP process using SPEM. The details for each phase are discussed in the following subsection.

Fig. 7 illustrates the use case diagram for analysis phase, whereas Fig. 8 represents the use case for early design and detailed design phase. Meanwhile, Fig. 9 illustrates the use case diagram for composition and implementation phase. These use case diagrams show the

Fig. 5. SPEM Icons

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106 Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training

In order to elaborate upon the preliminary hierarchy containment based on the finer-grain component, regular containment hierarchy is produced at the detailed design phase. Thus at implementation phase, the integration process is performed, and it is represented by the component composition diagram. Based on the component composition diagram, a code template is generated, which includes single component code template and component

The models and phases in this research are derived from MARMOT and PECOS models to create a new method; the MARMOT method is applied at analysis, early design and detailed design phase while PECOS is applied at detailed design, composition and implementation phase. The integration point between MARMOT method and PECOS component model is at

The central notion in COA and COD is the component. COP supports constructing software systems by composing independent components into software architecture. Software component is grouped with its component infrastructure. Component infrastructures include three elements, which are component model, component connection and component deployment. The analysis phase supports COA method, whereas early design and detailed design phases support COD. COP method is supported by two phases, which are composition and implementation phase. The next section provides detailed discussion on the process model of the integrated MARMOT and PECOS method. The process model

**4. A component-based development process model for embedded real time**

Defining the integrated process of MARMOT and PECOS methods in a systematic form is important to enable and support the development of CASE tool for ERT system. Here, the process is represented using Software Process Engineering Meta-model (SPEM) (Schuppenies & Steinhauer, 2002). SPEM is a meta-model that is used to describe a concrete software development process or family of related software development process. The Fig. 5

a. Activity: is the main subclass of Work Definition, it describes a piece of work performed

Fig. 7 illustrates the use case diagram for analysis phase, whereas Fig. 8 represents the use case for early design and detailed design phase. Meanwhile, Fig. 9 illustrates the use case diagram for composition and implementation phase. These use case diagrams show the