# Industry 4.0 and Prospects of Circular Economy: A Survey of Robotic Assembly
and Disassembly

Morteza Daneshmand, Fatemeh Noroozi, Ciprian Corneanu, Fereshteh Mafakheri,
Paolo Fiorini

###### Abstract

Despite their contributions to the financial efficiency and environmental
sustainability of industrial processes, robotic assembly and disassembly have
been understudied in the existing literature. This is in contradiction to
their importance in realizing the Fourth Industrial Revolution. More
specifically, although most of the literature has extensively discussed how to
optimally assemble or disassemble given products, the role of other factors
has been overlooked. For example, the types of robots involved in implementing
the sequence plans, which should ideally be taken into account throughout the
whole chain consisting of design, assembly, disassembly and reassembly.
Isolating the foregoing operations from the rest of the components of the
relevant ecosystems may lead to erroneous inferences toward both the necessity
and efficiency of the underlying procedures. In this paper we try to alleviate
these shortcomings by comprehensively investigating the state-of-the-art in
robotic assembly and disassembly. We consider and review various aspects of
manufacturing and remanufacturing frameworks while particularly focusing on
their desirability for supporting a circular economy.

##  I Introduction

TABLE I: A summary of the RAD-related concepts reviewed throughout the paper,
along with their linkages to the circular economy key performance indicators
listed in Table 20, as well as the indices of the corresponding references and
relevant sections.

Task Subtasks E1 E2 T1 T2 Eco1 Eco2 References Section Planning Robotic
Assembly and Disassembly Classical Sequence Planning [1] II-A Automatizing
Sequence Planning $\bullet$ [2, 3, 4, 5, 6, 7, 8, 9, 10, 11] II-B Mixed-Model
Problem Formulation $\bullet$ [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28] II-C1 Optimization Algorithms $\bullet$ [29, 30, 31, 32,
33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44] II-C2 Optimization Criteria
$\bullet$ [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55] II-C3 Evaluation
$\bullet$ [56, 57] II-C4 Learning Assembly Sequence Planning $\bullet$ [58,
59, 60, 61, 62, 63, 64] II-D Grasp Planning $\bullet$ [65, 66] II-E
Alleviating the Computational Burden [67, 68, 69, 70] II-F Executing Robotic
Assembly and Disassembly Types of Robots $\bullet$ $\bullet$ $\bullet$
$\bullet$ $\bullet$ $\bullet$ [71, 72, 73, 74, 75, 76, 77] III-A Object
Grasping and Manipulation [78] III-B Compliance [79] III-C Tackling
Uncertainties $\bullet$ $\bullet$ [80, 81, 82] III-D Simulating Execution
$\bullet$ [83, 84] III-E Human-Robot Collaboration $\bullet$ [85, 86, 87, 88,
89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99] III-F

TABLE II: Some of the most fundamental key performance indicators of a
circular economy as suggested in [56].

Index Key performance indicator Description E1 Environmental benefit ratio
Environmental impacts arising from material production, recycling or disposal
E2 Target material concentration Scarce materials and their concentration
within the components T1 Total disassembly time ratio Ratio of manual to to
robotic disassembly time for a given product T2 Complexity of the robotic
disassembly The number of robotic operations, tool-change operations and
joining methods Eco1 Disassembly cost and revenue ratio Proportional advantage
of the cost and revenue ratio of robotic disassembly with respect to the
manual one Eco2 Total profit from robotic disassembly Profit from robotic
disassembly, i.e. materials’ value after subtracting the associated costs

Automatizing product assembly and disassembly (a process known as [
([)4]RAD111Throughout this paper, RAD will be used to refer to Robotic
Assembly and Disassembly, in general. Whenever differences are important, the
more specific terms [4]RA or [4]RD will be used accordingly. (see Fig. 1) can
lead to enhancements in speed of production and general affordability [80].
[4]RAD has two important parts: _Planning_ and _Execution_. _Planning_ for
[4]RAD consists in coming up with a sequence of actions that, given an initial
configuration, would result in a desired final one [6]. This specific type of
planning is known as _Sequence Planning_. In the past, _Classical Sequence
Planning_ used to be done by highly qualified process engineers. A variety of
tools like computer-based platforms for creating and storing plans, generative
process planning and computer aided design interfaces have considerably
automatized the process. Sequence planning is a complex mathematical
optimization problem. In the earliest stages, solving it was solely based on
mathematical models. Gradually, [4]AI came into play, aiming at reducing
search spaces and alleviating the resulting computational burden by decreasing
the number of iterations required for coming up with an optimal or near-
optimal solution. This required rigorous problem formulations, optimisation
algorithms, criteria and evaluation and a careful consideration of
computational costs.
Upon availability of a plan, a robot would be in charge of _executing_ it.
There are a variety of available industrial robots each with specific
advantages for specific tasks. In order to execute the sequence planning,
these robots have to be able to manipulate 3D objects, which requires robust
visual perception for pose detection, advanced haptic, tackling uncertainties
and errors, and safely and efficiently collaborate with humans.

Figure 1: A product is assembled from a set of component parts. At the end of
its usage it can be disassembled and its components reused or recycled. Robots
and artificial intelligence can play a fundamental role in optimizing both,
with great benefits of costs, efficiency and environmental sustainability. The
figure has been adopted from [100].

Both planning and execution of [4]RAD can be facilitated by product design.
Reducing the number of parts, preference for certain geometric shapes,
improving accessibility of the robotic arm to parts’ attachments are just a
few attributes that could contribute to higher efficiency and lower costs.
Finally, [4]RAD has considerable economic, environmental, and social benefits.
It can greatly improve processes in re-manufacturing and recycling, which is
fundamental in today’s increasingly circular economy.
In the following, we offer an overview of [4]RAD. Sec. II is dedicated to
sequence planning. Sec. III details key execution aspects. Facilitating
planning and execution by design is the topic of Sec. IV. We continue with a
study of the importance of the underlying technologies for sustainability and
circular economy in Sec. V. The final section, Sec. VI, is dedicated to
conclusions and recommended future directions of study.
Moreover, a summary of the concepts reviewed throughout the paper has been
listed in Table I, along with their linkages to some of the most essential
circular economy key performance indicators introduced in Table II, as well as
the indices of the associated references and relevant sections.

##  II Planning Robotic Assembly and Disassembly

In this section, the most essential elements of planning processes for Robotic
Assembly (RA) and Robotic Disassembly (RD) procedures, which have been
summarized in Fig. 2, will be reviewed. For more in depth review of _Assembly
Sequence Planning_ the interested reader is referred to [1, 101]. _Disassembly
Sequence Planning_ is treated in [102], while a survey of studies on
_Disassembly Line Balancing Problem_ has been provided in [103, 104].

Figure 2: Planning of RA and RD processes in a glance. The figure has been
adopted from [105]. Common objective functions based on which the main steps
of [4]RA and [4]RD processes may be optimized are listed, where given certain
assumptions, each of the foregoing two might be reversible, resulting in the
other one. Assembly and disassembly SP procedures are aimed at the tasks of
devising sequences of collision-free operations resulting in attaching parts
to a product being assembled or removing them from the one being disassembled,
respectively. Moreover, the line balancing problem is targeted at deciding on
the choice and order of the operations to be left upon each of the
workstations. Last but not least, the task of path planning concerns finding
feasible paths following which each of the operations could be performed by
the relevant robot within the corresponding workstation.

###  II-A Classical Sequence Planning

[4]RA is concerned with converting pieces or parts from their original or
intermediate forms to the final ones. This typically involves tasks such as
machining, forming and joining. For joining different parts of a product,
mechanical fastening, adhesive bonding, soldering, brazing and welding are
among the most common types of approaches utilized.
The order in which these tasks are performed is an important factor for
efficiency and speed, resulting in higher quality and lower costs and required
amount of raw materials. This affects the product’s competitiveness.
Classically, it used to be done by a process (or assembly) engineer, who would
need to possess a variety of skills, e.g. to be familiar with the approximate
costs of the raw materials, tooling procedures and other processes, to
understand the role of the raw materials and fixtures, to estimate the time
and costs it takes to perform the processes, to be capable of using
machinability data handbooks and other references, as well as engineering
drawings, and be aware of what resources could be obtained from the shops.
Usually, a process planning procedure, which involves _sequence planning_ at
its core, is performed through iterating over the following steps: 1.
Understand the design specifications and the drawings, 2. Upon detecting the
datum surfaces, determine the setups, 3. Pick up the suitable machines for
each setup, 4. Decide on the sequence of operations for each setup, 5. Choose
appropriate tools for each process, 6. Design or opt for the relevant fixtures
for every setup, 7. Tune the associated parameters, 8. Make the final process
plan. It should be noted that failing to establish reasonable assembly
relations leads to various deficiencies, including a waste of the raw
materials [1].

###  II-B Automatizing Sequence Planning

A great fraction of early ideas aimed at automatizing process planning were
intended to deal with machining processes. It underwent four main phases.
First, to develop computer-based platforms making it possible to create, store
and retrieve reports and plans.
Second, computers were introduced, which could enabled storing plans
previously developed, and at the time a new one for a similar product needed
to be created, retrieve the foregoing plan, and then revise it according to
the associated characteristics.
In the third stage, an effort was made to obviate the necessity of the
presence of a process engineer, resulting in a generative automatized process
planning where the logic based on which decision-making needs to be performed
would be handled automatically using some form of [4]AI. Ideally it will not
demand the use of prior plans.
In the fourth phase, Computer Aided Design interfaces were emulated using
feature extraction for extracting information from drawings.
Nevertheless, despite the great amount of progress made in automatic matching
processes, the essential aim of eliminating the role of the human operator and
automatizing the whole process planning pipeline did not experience a
comparable level of advancement. This was due to a variety of factors,
including the particularities of every manufacturing system, the typical
enormousness of the process planning knowledge and the deficiencies design
models usually entail for process planning [1].
On the other hand, with the recent advances in augmented reality, it is
possible to develop semi-automated human-in-the-loop frameworks through
employing Computer Aided Design models within robotic simulation software.
Given proper calibration, it facilitates the process of programming the
assembly of small batch-sized products involving numerous parts in terms of
complexity, costs and programming time-consumption associated with practical
application [2].
Part agent systems, which take advantage of network agents, possibly along
with Radio-Frequency Identification and Augmented Reality technologies, enable
one to monitor the statuses of parts and products throughout their whole life.
They can offer pieces of advice regarding e.g. whether a certain part needs to
be changed for new, requires additional care, or is in a form that is good
enough for it to be retrieved and used in the remanufacturing of other
products. Moreover, such systems could facilitate disassembly processes by
storing and visualizing the relevant models [3].
Nevertheless, studies show that there is still a wide spectrum of assembly
tasks related to the manufacturing of mechanical products, which have not been
practically robotized, despite potential possibilities. For example, when it
comes to automobiles, only 10 to 20 percent of the relevant operations are
performed in an automated manner [4]. An example of classically manual
processes that are being gradually robotized and automated is sorting
disassembled parts which is taking more and more advantage of machine learning
technologies during the last few years [5].
Robots may be able to collaborate for planning the necessary operations and
performing the construction tasks required for making structures such as
shelters, bridges, buildings or overhang constructions from prefabricated
components, such as cylindrical parts, in disaster areas, which are unsafe or
hard to access for humans. Some of the challenges involved in task planning
for the foregoing purpose arise from the necessity of employing additional
components playing the role of counterweights or temporary supports during the
operation, preassembling moving structures or integrating prepared structures
into the final design.
On the other hand, on top of geometric feasibility regarding possible
collisions between the blocks, further feasibility checks are required for
avoiding collisions while trying to reach a certain block, as well as for
guaranteeing stability. In [6], answer set programming was utilized in order
to develop a hybrid planning system for multiple robots with the capability of
performing the aforementioned feasibility checks, including stability and
reliability, not only for the final structure, but also for each of the sub-
structures in every step of the process of construction.
One of the greatest applications of [4]RA is in constructing buildings through
Rapid Prototyping222A family of methods to build a scale model of a part or
product in a fast manner based on computer-aided design and Three-Dimensional
(3D) printing, or additive manufacturing [106, 107, 108]. by means of Robotic
Manipulators333Arm-like mechanisms consisting of series of links and joints
referred to as cross-slides, which are utilized for grasping and manipulation
without human intervention [109, 110].. The first attempts were made in 1980s
in Japan, and have, ever since, been vastly studied by researchers from the
USA, Germany and Switzerland. Such technologies are practically better
applicable to low-rise buildings, and take advantage of recent advances in
swarm robotics and 3D printing. Although many of the tasks required for
concreting, steel reinforcement and formwork could be left to robots,
horizontal Reinforced Concrete elements still cannot be created on-site and in
an automated manner [7].
The use of robotics in the construction of buildings takes special importance
whenever there is a shortage of skilled workers due to e.g. an aging
population, and manifests its advantages as higher speed and lower CO2
emission [8], as well as higher safety and less energy consumption than manual
operations [9]. It could accelerate the process by about 5 times, compared to
the case where the task is performed manually.
[4]RD of buildings, including their interior walls, has recently benefited
significantly from digitization technologies such as automated meta-data
generation and scan-to-Building Information Modelling444Digital
characterization of a place in terms of properties and functionalities,
throughout the whole life-cycle, i.e. from formation to demolition [111,
112].. One of the underlying challenges is to collect and process raw, non-
semantic data intelligently, aiming at an automated process of spatial
parameterization, which involves extraction, modification and analysis of the
spatial properties of a component.
Similarly to other applications of [4]RD, it will result in adaptive reuse of
the building, selective disassembly for repairs and rehabilitation,
reconfigurable, prefabricated assemblies and RA programming. Using a rule-
based algorithm, the 3D Cartesian properties and possible clashes between non-
semantic Computer Aided Design elements could be detected. Global spatial
constraints for disassembly, Building Information Modelling, Level Of
Development555The constituents and the level of robustness of different
elements of the Building Information Modelling at various stages [113]. and
modeling accuracy are among the factors which could affect the resulting level
of automation [10].
Moreover, selective disassembly planning through Multi-Objective Optimization
may lead to combinations of different deconstruction approaches, considering
the technical and economic constraints imposed by the underlying components
[11].
The idea of developing a Circular Economy (CE), which involves, among others,
design for assembly, as one of its main components, is applied to the Amstel
III district in Amsterdam, Netherlands in [8]. It is meant to develop modular
component, which could be connected to each other in a reversible manner. In
the aforementioned application, the materials from an office building have
been transformed to a mid-rise, mixed-use building that includes residential
compartments, as well as a library offering flexible workplaces, an exhibition
area and a restaurant.

###  II-C Sequence Planning

Generally, as illustrated in Fig. 3, the problem of sequence planning could be
thought of as _that of planning a sequence of actions which given an initial
configuration could obtain a final one_.

In what follows, the main concepts and procedures related to optimizing the
processes of [4]RAD will be elaborated, where the outcome will be a knowledge
model through which, among others, the assembly process, the relevant spatial
positions, the product structure and a set of desirable mating features and
hierarchical relations between the subassemblies are explained [114].

Figure 3: Graphical representation of [4]RA and [4]RD sequence planning, with
the process flow directions being shown using red and blue arrows,
respectively. The four pieces $p_{i}$, $i:\\{1,2,3,4\\}$, shown in this figure
are assembled in the sequence indicated by arrows, where e.g.
${p_{1}}\longrightarrow{p_{2}}$ means that $p_{1}$ is attached to ${p_{2}}$,
resulting in $p_{12}$.

####  II-C1 Mixed-Model Problem Formulation

Performing automated industrial RAD tasks often requires numerous robots,
organised in multi-robot assembly lines, to work together. Robots providing
different functionalities may be associated with different energy consumption
profiles and process execution times, whose operations need to be optimized
for a fast, efficient and reliable performance [12].
Numerous factors could be considered while trying to optimize the performance
of a multi-robot assembly line. This could be handled via a _mixed-model
problem formulation_ employed under an assembly line _balancing problem_ ,
considering the underlying complexity and disassembly modes as well. For
example, destructive disassembly modes, regardless of whether the part is
detachable or not, are assumed to be associated with high energy and time
consumption, and thus, believed to be of low value.
Reducing the amount of space required for [4]RAD tasks, as well as the lead
time, is among parameters which are negatively affected by the level of
automation, i.e. employing fewer operators leads to the necessity of larger
spaces and longer lead times. Thus the number of human operators could also be
considered as an adjustable parameter within the relevant assembly line
balancing problem [13].
The aforementioned parameters, i.e. the optimization objectives, may involve
e.g. CO2 emission rate, total load density, hazardous task cost, the number of
workstations and robots, the amount of resources utilized and the production
rate.
On the other hand, in the context of disassembly aimed at remanufacturing, the
ratio between the output value and the corresponding resources fed as input
provides a clear indication of the relevant quality and efficiency [14]. The
mixed model could be framed through mixed-integer mathematical programming
[15].
It is worth noticing that analyzing the complexity of the balancing problem
has been long performed based on the perspective proposed in [115], but this
has recently been questioned due to the fact that the foregoing study was
published before the concept of NP666Non-deterministic polynomial
time.-hardness was introduced [16].
In essence, _sequence planning is a multi-objective discrete optimization
problem_ , which needs to be dealt with as an NP-hard combinatorial one, where
one needs to find the set of all feasible sequences, in the sense that the
outcome has to be collision-free and that the robot has to be able to carry
out the task of assembly based on the sequence planned [17].
Unfortunately, intractably large search spaces and local optima are among the
known problems arising while trying to handle the foregoing task. To remedy
this, stability graphs could be utilized for coming up with stable assembly
subsets, which reduces the search space to a smaller one. Moreover, the
fitness of the subassemblies could be analyzed at each stage, according to the
user’s preferences, so as to further reduce the search space [18].
One of the most crucial criteria in this regard is the level of freedom in
movement which is provided to the robot or the cooperating human being. An
essential constituent of such an analysis is a Disassembly Interference Graph,
which describes the obstruction relations between the parts. It can contribute
to performing sequence planning such that the assemblies are partitioned with
access to the parts taken into account as a major priority, i.e. considering
minimizing blockage of the parts as the target. Consequently, a series of
subassembly decomposition is developed in the form of a tree structure that is
suitable for parallelization [19]. It is worth noticing that parallelization
of assembly or disassembly tasks requires ensuring the stability of the
subassemblies, which demands inferring and taking into account extended
subassembly stability relationships, aiming at avoiding possible undesired
divergences of the main product structure during the course of performing
[4]RAD [20].
One of the fundamental topics in the context of sequence planning is
generating assembly precedence constraints, which could be performed based on
3D models in a simulation environment, using block sequences, which enhances
the quality, and reduces the costs associated with developing Virtual Assembly
software. Virtual assembly of a four-coordinated mobile robot has been
reported in [21]. Design and assembly of marine diesel engines is another
instance of the applications of the technologies discussed throughout this
article, which could be performed through visual 3D simulation [22]. The
development of a robotic reconstruction site has been reported in [23], where
sensors have been utilized for simulating real-world data through a virtual
environment. Learning to assemble a toy with building blocks from
demonstrations by expert users through Virtual Reality and process mining has
been presented in [24].
In the above context, the block sequence structure is meant to store and
process the reference variables of the paths and the attributes resembling
assembly precedence constraints. In order to bypass the requirement of
preprocessing each part model, assembly reference objects are considered as
nodes for distributed management of the aforementioned paths. This way,
preprocessing and calculation are performed on the precedence relations rather
than a particular assembly or disassembly sequence. Such a software could be
developed together with the relevant state-transition functionalities using
the procedures introduced in [25], where the required parameter-input
interfaces are available for a variety of simulation platforms.
The main challenges involved in computer-aided process planning arise from the
economical, technological and geometrical constraints. Decomposing the problem
into smaller ones, including macro-level problem targeting and managing the
general process and micro-level ones concentrating on the details of
individual assembly or disassembly operations, may contribute to reducing the
foregoing complexity. To this end, a constraint model could be used, where
assembly operations, as well as the whole product, are resembled through
feature-based representations, which constitute the macro-level problem. On
the other hand, feedback from micro-level, which is represented as feasibility
cuts, helps integrate the solutions to the problems into a single, efficient
computer-aided process planning package [26].
Traditionally, an experienced design engineer would need to extract the
properties of the suitable [4]RAD process of the product, as well as its own
features, from the associated Computer Aided Design models. However, the
foregoing task has greatly advanced during the last few decades, and has
gradually become more automatic within typical industrial settings, which is
based on product liaison matrices based on symmetric feature detection, and
results in a standard pose and orientation exchange file format [27].
On the other hand, at the design stage, the trajectory following which each
part needs to be taken from the original pose and orientation to the one at
which it could be mounted in a collision-free manner has to be determined in
advance. Achieving the foregoing objective may require creating a multi-
directional combined trajectory, which could be realized through employing the
bounding box approach and the notion of position matrix [4].
Furthermore, the equipment required for an assembly or disassembly line
constitute a significant factor in estimating and comparing different
alternatives of doing so. Thus the choice and configuration of the equipment
could be modeled and analyzed in conjunction with the main assembly line
balancing problem, in order to enhance the chances of succeeding in reducing
the overall cost associated with the design of an assembly/disassembly line,
which could be formulated as a Mixed Integer Linear Programming problem [28].
In this context, the latter refers to an optimization program some of whose
variables are bound to take only integer values, where the objective function
and the constraints are linear [116, 117]. According to [118], assuming that
$\mathbf{X}$ and $\mathbf{Y}$ are column vectors with the components $x_{i}$,
$i=1$ to $p$, and $y_{j}$, $j=1$ to $q$, which stand for the continuous and
and integer variables, respectively, and $\mathbf{A}$ and $\mathbf{B}$ are
rectangular matrices of sizes $(m,p)$ and $(m,q)$, respectively a Mixed
Integer Linear Programming may be mathematically formulated as follows:

$\displaystyle\min_{\mathbf{X},\mathbf{Y}}$
$\displaystyle\mathbf{F}=\mathbf{A}_{0}^{\text{T}}\mathbf{X}+\mathbf{B}_{0}^{\text{T}}\mathbf{Y},$
(1) s.t. $\displaystyle\mathbf{A}\mathbf{X}+\mathbf{B}\mathbf{Y}=\mathbf{D},$
$\displaystyle\alpha_{j}\leq y_{j}\leq\beta_{j},$ $\displaystyle y_{j}\text{
integer},$ $\displaystyle j=1\text{ to }q,$ $\displaystyle 0\leq x_{i},$
$\displaystyle i=1\text{ to }p.$

which can be solved for an optimal solution, possibly among several of them,
using Dynamic Programming. The latter refers to a family of useful tabular
algorithms for solving optimization problems involving overlapping
subproblems, i.e. sharing subsubproblems. In order to solve the main problem,
Dynamic Programming resorts to combining solutions to the subproblems. To
avoid computing solutions to the subsubproblems recurrently, it stores them
into a table.
Typically, a Dynamic Programming algorithm comprises of, first, identifying
the problem’s structure, and then defining the value of an optimal solution
recursively, being pursued by computing the value of the optimal solution in
the aforementioned bottom-up fashion. If needed, the corresponding solution
itself may also be found based on the resulting information, being, in some
cases, supplemented by additional data kept for a more efficient computation
[119].

####  II-C2 Optimization Algorithms

Generally, optimizing the design and performance of robotic systems is
inspired by evolutionary processes and their dynamics. The foregoing
approaches may be categorized based on whether they try to develop a new
solution or aim at improving upon the ones that already exist. Nevertheless,
typically, most of them prefer to find a compromise between the two, i.e.
exploration and exploitation, through incorporating intrinsic mortality, which
is connected with the mutation rate as well. Similarly, a trade-off needs to
be found between genotype and phenotype mapping, referring to the direct or
generative manner of expressing the genomic information, respectively.
Lindenmayer systems (L-systems) could be used for developing a generative
encoding enabling to make virtual creatures inspired by plants. In the
particular case of modular robots, generative encoding have played a major
role in development by enabling mapping the genotype to the phenotype. It has
been shown to result in better locomotion performances compared to direct
encoding. For example, adding extra solar panel modules for energy autonomy
could provide inspiration for the evolution of various types of robots, an
example being knife-fish-inspired soft robots [29].
Recent advances in soft computing have contributed to improving the efficiency
of Optimal Assembly Sequence Planning algorithms in terms of CPU times and
fitness values [30]. Nevertheless, typically, the greatest challenges involved
in finding out an optimal or near-optimal solution in the context of assembly
sequence planning arise from intractable largeness of the search spaces, which
results in longer execution procedures, and the possibility of getting stuck
at local optima or dead ends, greatly affecting the performance and robustness
of methods such as Labeled Real-Time Dynamic Programming, policy iteration or
value iteration [31].
Disassembly line balancing problems, including the ones involving multiple
products with different models, are NP-hard, as a special case of the bin
packing problem [16], and could be solved using mixed-integer mathematical
programming [12], followed by utilizing a variant of genetic algorithms, which
makes use of chromosome evolution rules, e.g. operators performing mutation,
crossover or selection [32]. For example, the Many-objective Best-order-sort
Genetic Algorithm, which involves its own neighborhood search operator, two
genetic operators, encoding/decoding scheme and best-order-sort mechanism,
could be utilized [15]. An alternative approach could be to apply meta-
heuristic simulated annealing evolutionary optimization [33].
When optimizing the performance of multi-robot disassembly lines, it should be
duly taken into account that different robots may be associated with different
energy consumption rates and task times. On the other hand, in the cases of
uncertainties in task processing times, intervals representing upper and lower
bounds on them, which may be known from experimental and physical conditions,
could be considered instead [33].
Being motivated by environmental sustainability concepts, the tasks of
assembly sequence planning needs to be performed such that the profitability
and energy consumption could be taken into account as concurrent optimization
criteria, which gives rise to problems referred to as Profit-oriented and
Energy-efficient Assembly Sequence Planning [34].
On the other hand, multi-objective evolutionary optimization could be
considered for solving a mixed-model balancing problem for multi-robot
workstations. Among other factors that affect the performance such frameworks,
the manner of initialization and variation operator taken into account could
be mentioned, which may be devised in a problem-dependent fashion [35].
Ternary bees algorithms constitute another category of the optimization
techniques which could be utilized in this context. The virtues of meta-
heuristic methods and greedy search could be resorted to simultaneously, based
on concurrent operations and collaborative potential solutions [36]. Another
relevant example is the dual-population discrete artificial Bee Colony
algorithm [14].
The Disassembly Sequence Planning problem could also be handled using, for
instance, the Modified Discrete Bees Algorithm based on Pareto [37].
Nevertheless, it should be noted that for scheme selection and recovery route
assignment, once obtained, Pareto-optimal sets of solutions resulted from
hybrid meta-heuristic multi-objective optimization algorithms that are based
on symbiotic evolutionary mechanisms need to be further studied through
decision-making processes such as the fuzzy set method, in order to pick up
the best possible solutions, which may rely on the preference factors supplied
by the user or operator, and aims at picking up options which are, at least,
non-inferior to the rest of the solutions [38].
The improved fruit fly algorithm is also an alternative to the problems of
optimal Assembly Sequence Planning [39]. Other studies have reported
successful application of Petri Net, as well [40]. The flower pollination
algorithm, which employs discrete operations and four heuristic rules, is
another option for solving such multi-object optimization problems [41]
Finding an optimal or near optimal solution from the search space could also
be performed using Markov Decision Processes, where heuristic search and
determination based on on-line resolution help achieve higher robustness in
comparison to methods such as Labeled Real-Time/Near Real-Time Dynamic
Programming and optimal control. In the foregoing settings, hierarchical sub-
tasks of a simpler and smaller nature, which may be structured automatically,
improve the feasibility and reliability of the relevant procedures. More
clearly, decision-making for probabilistic planning could be performed
according to stochastic shortest path problems being dealt with using Markov
Decision Processes [31].
Disassembly line balancing problems could be solved using classical software
packages, such as IBM ILOG CPLEX Optimization Studio [120], or alternatively,
a branch, bound and remember algorithm with AND/OR precedence. The memory-
based dominance rule could be utilized for removing duplicate sub-problems,
where cyclic Best-First Search could be resorted to in order to accelerate the
process of coming up with high-quality, complete solutions [42].
In [43], the Elephant Search Algorithm has been used for optimizing the number
of [4]RA levels in the context of Optimal Assembly Sequence Planning.
Hybrid Cuckoo–Bat Algorithm, Ant Colony Optimization, Grey Wolf Optimization,
Advanced Immune System and Hybrid Ant–Wolf Algorithm are other instances of
optimization approaches that have been applied to the problems of Optimal
Assembly Sequence Planning in the literature [30], along with Bidirectional
Ant Colony Optimization as an alternative approach with a relatively higher
efficiency than the aforementioned algorithms [44].

####  II-C3 Optimization Criteria

A [4]DLBP is aiming at allocating resources to processes in an optimal manner
[27]. Numerous types of criteria or combinations of them could be considered
when devising and solving such a problem. Common choices include minimizing
total and peak workstation energy consumption, as well as cycle time and the
number of robots operating simultaneously [35]. More clearly the disassembly’s
financial benefits, i.e. the recovery profile, and the consequent
environmental and economic impacts, and energy consumption are considered
conflicting objectives. Finding a balance between them needs to be
investigated based on a set of prescribed preferences [38].
It is possible to reduce the energy consumption associated with the
manufacturing of a certain product by optimizing the acceleration profile,
without sacrificing the speed and convenience, i.e. with the same production
cycle and equipment [45].
Moreover, if applicable, the cost associated with hazardous tasks could be
considered as an extra objective [12]. In contexts such as the assembly of re-
configurable structures, other common optimization criteria include fault
tolerance and the total distance travelled [54].
Other factors involved in typical [4]DLBP include the consistency in the
amount of idle time that different workstations experience, as well as fast or
prior manual removal of highly demanded or dangerous parts.
On the other hand, a change of the tool or its current operational direction
could also be considered as costs in the contexts of Optimal Assembly Sequence
Planning. Similarly, the disassembly feasibility of each part could be taken
into account for choosing the operations that e.g. are easiest, or result in
the disassembly of the most wanted parts. This has given rise to indices
formulating the operational time and cost, which will be introduced in what
follows.
Although most of the relevant studies reported in the literature heretofore
have considered the time and cost associated with the [4]RA procedure as the
main criteria for Optimal Assembly Sequence Planning, a number of additional
factors may result in the outcomes of the foregoing calculations being
rendered unrealistic or inaccurate. First of all, despite the prevalent
assumption that the product needs to be disassembled completely, this might
not be the case under various scenarios, which implies that a partial
disassembly of the product, depending on the underlying purpose, may need to
be considered and evaluated. Second, the time and cost may depend largely on
the [4]AS taken into account, where some of the existing indices may fail to
provide a sound, reliable and effective evaluation and comparison of all
possible orders in which the operations are thought to be performed. Third,
End-of-Life777A product at the end of its useful life. products of the same
type may be in different conditions at the stage of [4]RD, which may also
affect the extent to which the results of the above analyses corresponds to
the reality.
To address the above shortcomings, on top of the _Disassembly Cost Index_ ,
which assesses the cost and time of the [4]RD procedure, indices such as
Disassembly Handling Index and Disassembly Operation Index have been
introduced, aiming at evaluating the feasibility and and complexity of the
relevant operations. They result in easier and faster decision-making through
obviating the necessity of actually disassembling the [4]EoL completely, as
well as making it possible to discriminating between the efficiencies of
different [4]RD procedures aimed at [4]EoL products of the same type being in
non-identical conditions. Furthermore, the _Disassembly Demand Index_ has been
suggested for finding out the priorities of different components being
disassembled from an [4]EoL, based on the demand for each of them [51].
A _Decision Support System_ utilized in the context of [4]RAD is aimed at
finding a compromise between the number of tasks and the platforms’ variety,
as well as customizing the selected platforms for the target product variant.
Fast, high-variety, customized manufacturing, from a broad perspective,
requires platform design. For example, _Median-Joining Phylogenetic Networks_
, which have classically been utilized in biology for predicting living
species’ ancestry based on the gaining or losing of the genes [121], and later
adopted in this settings in [122], may help determine the number and
composition of the platforms. Providing a chance to consider a flexible number
of products, by means of phylogenetic tree decomposition, which necessitates
establishing product family phylogenetic networks, different variants of a
product may similarly be achieved through gaining or losing of the components.
Consequently, trade-offs need to be found, at each level, between the number
of [4]RAD tasks required for producing a customized variant of the product and
the number of platforms, which represents the extent of variety, implying
reductions in the delivery times and costs. Such analyses and comparisons
could be performed using indices such as _Platforms Reconfiguration Index_ ,
which represents the effort required for changing the configuration to a
different variant, based on the number of assembly and disassembly tasks
involved, as well as _Platforms Customisation Index_ , which represents the
ease of the assembly and disassembly factors. The results may also be useful
in terms of making a decision as to whether to consider Delay Product
Differentiation or Assemble to Order strategies for a specific product variant
[46].
Other optimization criteria involved in mixed-model problem formulations for
[4]OASP and [4]ODSP include load balancing indices and frequency and costs of
invalid operations [47].
Moreover, obviously, the speed and rate at which products are assembled or
disassembled plays a major role in the overall success and efficiency of the
associated pipeline. This, in turn, depends on a variety of factors, including
the type and number of fasteners, the type of the material from which the
product is made and its weight, geometric shape and center of gravity. In
fact, such analyses take importance even in the stage where the product has
not been designed yet, since the outcomes will help more quickly come up with
a suitable assembly strategy. In the foregoing context, design attributes and
features are converted to standard time data in the form of Time Measurements
Units, using Time Measurements Methods [48].
On the other hand, estimating the time [4]RD will take to be performed on e.g.
an [4]EoL product is required for conducting decision-making. At the detail
stage, it may be estimated using the Boothroyd and Dewhurst classification
approach, which provides a framework for systematically representing various
features associated with each part, playing a role in operations such as
fastening, insertion, orientation, movement and acquisition, as well as other
constituents of the procedure which do not concern a certain part, such as
terminating the [4]RAD procedure [123]. Acquisition, manual processing and
insertion constitute typical operations and the time each of which will take
needs to be estimated. Since component symmetry features are usually
irrelevant in the context of [4]RD, the remaining procedures whose required
times need to be estimated involve part-removal and replacing time, as well as
other environmental variables such as accessibility and visibility, along with
all other problem-specific factors. Experiments involving 31 undergraduate
students revealed that the difference between the time estimated based on the
strategy described above and the time it took to manually perform assembly and
disassembly had differences as small as 7.4% and 2.4%, respectively [49].
Different properties of the parts involved in each product need to be taken
into account, such that the [4]RA time could be estimated reliably. Wear, rust
and deformation are some of such characteristics, which could be represented
by corrective factors that are derived using a data mining procedure based on
a relevant demonstration of disassembly. For example, this has been performed
on the water pump of a coffee machine and the electric motor of a washing
machine with maximum deviations of 3% and 6%, respectively [50].
It should be noted that the [4]RD time associated with different [4]EoL items
of the same product may not all correlate precisely, due to the partially
unpredictable statuses of [4]EoL products [51].
Finally, the problems of [4]OASP or [4]ODSP may be subject to various
constrains stemming from e.g. the precedence constraints or the resources,
such as tools or operators, being limited [52].
In order to achieve lighter weight and greater flexibility by means of
incorporating tensile elements, the stability of each component of a structure
meant to be used in zero-gravity conditions may be arranged based on a balance
between the compressing forces pushing on it and the tensile ones pulling on
it, which would appear to try to shorten or extend it, respectively. Tensional
Integrity (shortened _Tensegrity_) takes advantage of a continuous network of
tension components, where the compression components are discontinuous, in
contrast to those of typical man-made structures [124].
Tensegrity structures (illustrated in Fig. 4) could be utilized for various
outer-space applications. They can be assembled using sampling-based motion
planning, where forward elimination search is used for estimating the pose and
orientation of the structure with respect to the robot. Subsequently, a
feasible assembly sequence can be obtained using backward disassembly search,
followed by creating motion plans for attaching strings to the bars using
rapidly-exploring Random Trees, in order to come up with the desired
configuration [53].

Figure 4: A Tensegrity robot. Figure from [125].

Assembling large-scale reconfigurable structures constitutes a significant
application of robotic assembly. It is important in launching applications
that could not be realized instantaneously. Its benefits arise from the
capability of assembling modular structures, which make it possible to
traverse and revise the configuration. In fact, in the foregoing context, the
problem is defined as that of transforming a structure from the original
configuration to a goal one [54].
The scope and practicability of outer-space missions could be greatly enhanced
through utilizing on-orbit robotics and assembly, whereby, outer-space
exploration hardware could be reused for a lower cost. Studies such as [55]
have shown that the assembly and performance efficiency may be kept roughly
constant while incorporating infinitely scalable reconfigurability by
employing modular materials, e.g. reversibly assembled cellular composites.
Moreover, they have concluded that discretely assembled materials and
reversible mechanical connection hardware lead to identifiable, bounded and
tolerable parasitic mass penalties. On the other hand, the energy consumption
associated with on-orbit tasks, such as conventional forming procedures,
additive manufacturing through deposition, recycling, reuse and
reconfiguration, could be significantly alleviated by means of taking
advantage of systems comprising of re-configurable materials.

####  II-C4 Evaluation

As aforementioned the Multi-Objective [4]DLBP needs to be solved keeping in
mind that a balance is required between multiple criteria, including economic
reasonability, technological efficiency and feasibility, favorability from the
point of view of environmental sustainability. The quality of the underlying
trade-off may be assessed using the Decision Support System proposed in [56].
On the other hand, the quality of an [4]AS or [4]DS could be evaluated based
on the underlying complexity, whose importance arises from its effect on the
extent to which automation could be realized. For example, the complexity of
tasks such as part feeding, mounting and handling, as well as implementing
provisions related to human safety, are affected by the aforementioned
complexity, and influence the time required for deployment or changeover
processes. The complexity depends on the task descriptions and the parts’
physical attributes [57].

###  II-D Learning Assembly Sequence Planning

Two-dimensional geometric planning for e.g. operations consisting of one-step
linear translations could be performed using Convolutional Neural Networks
(CNN)888A special type of neural networks which have convolutions instead of
matrix multiplications within at least one of their layers [126, 127]., where
a robustness score, i.e. success probability, is attributed to each assembly
motion by adding stochastic noise in a simulation environment. In fact,
utilizing CNNs obviates the necessity of performing simulation for all of the
planned motions, which takes an order of magnitude longer, by mapping each
motion to a robustness score, based on a pair of images of the subassemblies
before and after they are mated [58].
Intractable expansion of assembly relations and sparse rewards are among the
known challenges associated with the use of Deep Reinforcement Learning999A
machine learning strategy taking simultaneous advantage of reinforcement
learning and deep learning, where the former concerns the ability of a
computational agent to learn how to make decisions through trial and error,
i.e. to figure out what to do in order to optimize an objective function, and
the latter helps it to do so based on large unstructured input data, obviating
the necessity of manual manipulation of the state space [59, 128]. for [4]ASP,
which may be alleviated through employing parameter transfer and curriculum
learning [60].
For example, the application of human-robot interaction in RAD is demonstrated
by the the Form2Fit system, which involves robot learning to assemble a kit
from a demonstration of its disassembly. This has led to an average
performance rate of 90% under various initial conditions on kit and object
pose and orientation, 94% under new configurations of the same kits, and 86%
in the case of unfamiliar objects [61].
Moreover, a furniture assembly system has been introduced in [62], where the
practical assembly of a table has been presented as a case-study. Similarly,
performing construction tasks based on demonstrations by multiple tutors has
been implemented and reported [63]. Last but not least, case-studies on the
[4]RA of white goods through [4]HRC have also been reported in [64].

###  II-E Grasp Planning

When it comes to performing the planned sequence of [4]RAD tasks, one needs to
determine the robot configuration through which it could grasp the relevant
part and carry out the rest of the necessary transfers while ensuring the
feasibility of the planned actions in terms of e.g. collision-avoidance
through performing a search strategy over a connected constraint graph aiming
at solving a Constraint Satisfaction Problem. The shape the foregoing problem
takes is affected by whether or not Human-Robot Collaboration is involved, as
well as on the number of robots involved [65].
_Grasp planning_ aims at finding the optimal posture of a robotic hand or
gripper, as well as the relevant contact placement, while grasping an object
or a part [129].
Assuming that multiple grasp positions and orientations may be available, one
part of the task of planning needs to be dedicated to picking up the most
suitable ones depending on the initial and final poses, as well as the rest of
the environmental variables, including the part of the product which has
already been assembled.
An orientation graph may be created which under the absence of feasible poses
from the initial to the final poses could generate a series of intermediate
poses which could approach the foregoing aim while avoiding collisions.
Moreover, in the case of disconnections from the tree, which may happen to a
node and its descendants due to the collisions taking place because of the
changes in the task space over each step of assembly, the connection needs to
be reestablished for further extension of the tree. This could be realized
using algorithms such as the two-stage extended Rapidly-exploring Random Trees
[66].

###  II-F Alleviating the Computational Burden

Although the computational load associated with RAD tasks may be large,
various strategies have been proposed to enhance the feasibility of the
relevant systems.
One way is to incorporate geometric reasoning and planning into the pipeline
handling symbolic task planning and assembly planning. This way, the high-
dimensional nature of the problem resulting from e.g. the high number of
Degrees-of-Freedom101010The number of independent parameters based on which
the configuration or state of a mechanical system may be defined [130, 131].
of the robot or from the complexity of the underlying tasks will be accounted
for.
It should be noted that in the presence of considerations related to secondary
parts, such as fits, washers, bolts and nuts, one may notice a considerable
impact of whether or not taking them into account as primary ones on the
computation time [67].
When it comes to [4]ASP for new variants of a given product, in order to
accelerate the process of sequence planning, it might be possible to develop a
system that could perform the foregoing task automatically based on the
product specifications, thereby obviating the necessity of the time-consuming
and labor-intensive task of doing it manually. Nevertheless, checking for
collisions requires performing simulations, which usually slows down the whole
process.
Alleviating the aforementioned problem could be realized through trying to
avoid unnecessary slow checks in the cases where it is possible to find out a
violation of the feasibility requirements from faster checks performed at
lower levels of fidelity. This could be achieved by iteratively revising
feasibility checks at different levels, i.e. from a level considering parts
only, which could be performed relatively fast, to high-fidelity ones such as
motion planning or full robotic simulations, which are comparatively slow.
Moreover, propagating the errors to the higher levels as symbolic rules, they
could be taken into account for further reducing the search space, i.e.
pruning the search tree [17].
Parallel assembly helps distribute the tasks to multiple processes, thereby
reducing the required time and enhancing the efficiency associated with the
[4]RAD of products consisting of multiple sub-assemblies. In this context, the
number of assembly levels directly affects the assembly cost and time. To this
end, parallel assembly possibilities need to be detected through sub-assembly
identification techniques [43].
Numerous studies have attempted to reduce the computational time required for
the complex [4]ASP or [4]DSP of products consisting of a large number of
parts. Examples include the linear-weight Scatter Search algorithm for Multi-
Objective Optimization or the lexicographic Scatter Search approach introduced
in [52].
Although numerous alternatives for solving an [4]OASP exist, the product
design itself plays a major role in both elevating the complexity of the
problem and the chances of arriving at desirable solution. Therefore, the
concept of Design for Assembly, being combined with the [4]OASP, may
contribute to the feasibility and success of the planning [4]RA through e.g.
reducing the number of parts. The integrated problem could be solved using
optimization methods such as Ant Colony Optimization, considering directional
changes as the fitness function [68].
On the other hand, the computational requirements associated with Constraint
Satisfaction Problems dealing with grasp planning may quickly become
intractable when number of operations increase. The foregoing issue may be
alleviated for achieving a faster performance than that of directly solving
the connected constraint graph through dividing the Constraint Satisfaction
Problems into a set of independent smaller problems. More clearly, the set of
feasible re-grasps over which the search needs to be performed could be
reduced by focusing on local possibilities and removing an increasing number
of re-grasps per iteration. Nevertheless, it should be duly taken into account
that under certain circumstances, the cost related to the elimination of the
re-grasps may be even larger than executing them. In such cases, the process
of removing the re-grasps could be stopped, followed by executing the existing
best plans [65].
When it comes to trajectory planning, purely geometrical models may become
computationally costly for severely constrained environments or complex
products. It has been shown that incorporating higher-level abstractions
involving multi-layer environment models containing semantic and topological
aspects within a global planner may alleviate the foregoing issue, where the
initial coarse model is locally refined based on semantically characterized
geometry [69].
On the other hand, knowledge transfer frameworks enable platforms allow
already acquired information regarding e.g. constraints to the new one using
which a newly customized product needs to be robotically assembled or
disassembled. In fact, the advantage of the foregoing strategy arises from the
possibility of restricting the [4]RAD search space based on the prescribed
constraints, thereby reducing the amount of required computations. Algorithmic
implementation of such an approach involves deducing semantic assembly
constraints based on feasibility, performing semantic feature-based graph
matching over the assemblies, and finally, employing classification and
pattern recognition in order to map the existing knowledge of subassembly
constraints to full assemblies [70].

##  III Executing Robotic Assembly and Disassembly

A review of systems aimed at assisting the operator with the task of assembly,
such as [4]HRC and context-aware instructive frameworks, has been reported in
[132]. For a review of _[ 4]HRC systems for disassembly_ the reader is
referred to [133].

###  III-A Types of Robots

In this section, we will introduce some of the most common types of industrial
robots with applications in RAD. An overview is shown in Fig. 5.
A study of the role of dual-arm robots in manipulation tasks involved in [4]RA
procedures has been reported in [71], where the integration of online
identification and hardware mechanism, which is required for compliance, as
well as perception by means of employing sensors, has been discussed as
essential components of such a system.

Figure 5: Sample industrial robots with applications in RAD: (a) A Unimate
robot with polar configuration [134], (b) An articulated robot in a foundry
[135], (c) A gantry robot [136], (d) A benchtop cylindrical microplate handler
robot for lab automation [137], (e) A Selective Compliance Assembly Robot Arm
[138], (f) A Codian Robotics delta robot [139], (g) A prototype machine of the
TriMule® robot [72], (h) A _Wheeled Mobile Robot_ within an
Assembly/Dissasembly Mechatronics Line [74].

_Unimate_ , the first industrial robot, was based on a polar, i.e. spherical
configuration that, as shown in Fig. 5, was tailored to the hydraulic drive
utilized for powering it. It involved five joints, including two rotary joints
at the base and the shoulder, a prismatic joint for extending or retracting
the arm, and two more rotary joints at the wrist. Nevertheless, these were not
enough for orientating the tool as desired, given that six degrees of freedom
were required which was not feasible, due to the incompetence of the relevant
control technologies of the time [140].
The most prevalent category of today’s industrial robots are _articulated_
[141]. These are often referred to as manipulator arms or robotic arms [142].
As shown in Fig. 5, they are inspired by human arms, and possess the
capability of performing various motions, owing to their high degrees of
freedom.
_x-y-z robots_ , which are also referred as _rectilinear, gantry or Cartesian
robots_ [143, 141], are used in a wide range of applications. They take
advantage of three prismatic joints and three rotary joints for achieving the
desired spatial position and orientation, respectively (see Fig. 5). For tasks
being carried out in a planar workspace, only three of the total six degrees
of freedom suffice, two for the position and one for the orientation [135].
Another category of industrial robots consists of _cylindrical coordinate_
ones. They involve a rotary actuator at the base, as well as one or more
prismatic joints connecting their links to each other. As illustrated in Fig.
5, by the virtue of the foregoing structure, they are capable of moving both
horizontally and vertically through sliding. Moreover, taking advantage of
compact End-Effector designs, they can reach narrow workspaces conveniently
[141, 137].
As a class of exemplary RAD machines, Selective Compliance Assembly Robot Arm
(SCARA) constitute another configuration of industrial robots, which, as shown
in Fig. 5, possess two rotary joints that are parallel to each other, aiming
at high-precision motion and positioning within a horizontal plane. They
support a rotary shaft which holds the End-Effector in the vertical
orientation [143].
All categories of industrial robots discussed are serial ones, which
notoriously suffer from the accumulation of the errors throughout the chains,
i.e. the series of links and joints connecting their base to the End-Effector,
resulting in less rigidity with an increased length of the chain. Therefore,
parallel configurations also exist, which involve more than one chain.
In addition to the fact that the chains involved in a parallel manipulator are
relatively simple and short, which makes them less prone to inaccuracies, the
fact that their motions, as well as possible off-axis tolerances, are
restricted by the rest of the chains in the underlying closed-loop fashion
further contributes to preventing them from undesired movements, where the
errors are averaged, rather then being accumulated.
For example, as shown in Fig. 5, delta robots posses a parallelogram linkage
structure, involving a four-bar configuration, and are suitable for high
maneuvering tasks, e.g. fast Pick-and-Place operations, and direct control
procedures [135].
The _TriMule_ ® is another robot that has been successfully utilized for
performing machining, e.g. drilling, tasks on large parts in the context of
[4]RA, due to its improved stiffness and accuracy [73]. It possesses a compact
5-degrees-of-freedom structure, consisting of a 3-degrees-of-freedom parallel
positioning manipulator and a 2-degrees-of-freedom wrist [144] (see Fig. 5).
In a [4]RAD Mecatronics Line, _Wheeled Mobile Robot_ , as shown in Fig. 5, may
work in two different ways. One strategy, which is suitable for products
requiring transporting parts with various weights, concerns the Wheeled Mobile
Robot working in parallel, where e.g. one performs transportation tasks, and
the other one carries out manipulations. Alternatively, the robot could be
involved in doing both transportation and manipulation tasks, each following
its own trajectory, where one robot is responsible for even workstations, and
the other one for the odd ones. By doing so, it will be possible to optimize
the whole production cycle uniformly. It should be noted that for accurate
localization of the Wheeled Mobile Robot, utilizing synchronization signals
could be considered, obviating the necessity of visual serving systems [74].

_Dual-arm robots_ have attracted serious attention from industrial and
academic communities during the last few decades, and are widely thought to
play a significant role in various [4]RAD tasks. Bi-manual Baxter robots could
serve as a reliable example [62]. They are used in multi-robotic construction
scenarios as well [6].
_Cable-driven Parallel Robots_ , on the other hand, are greatly flexible, and
their modularity and reconfigurability paves the way for their substantial
applications in [4]RAD. More clearly, the possibility of manually attaching or
detaching a desired number of modular branches enables to adjust their spatial
topology as required, where modifying the connection points on the End-
Effector allows for applicability to a further diversified spectrum of [4]RAD-
related operations. The vector closed rule and the Lagrange method constitute
essential elements in analyzing, understanding and solving the underlying
Inverse Kinematic Problem111111Calculation of the required values of the joint
parameters for the End-Effector (EE) to appear at a certain pose with respect
to the base [145, 146]. using Inverse Dynamic Programming121212The maximum
(minimum) of a problem is the inverse function to the minimum (maximum) of the
inverse problem [147]., which is a prerequisite for making use of the robot in
an effective and efficient way [75].
The usage of the ABB IRB 140 robot for executing [4]RA tasks through a desired
trajectory has been reported in [76]. Sawyer, which is a robot from Rethink
Robotics is a suitable choice when it comes to collaborative tasks.
A typical [4]RAD setup for Waste Electrical and Electronic Equipment may
consist of a two-axis manipulator and a wave soldering machine, which are
coordinated for motion using a Central Control Unit, accompanied by a suction
cup for picking components [77].

###  III-B Object Grasping and Manipulation

During [4]RAD, the robot has to be able to grasp the part at hand, as well as
relocate and reorient it, depending on the nature of the underlying task. In
either case, it necessitates keeping track of the part’s 6D pose. The most
common approach to this problem is vision-based. Setting it up demands making
a database of pose examples for traininng a statistical model (most commonly a
deep neural network), as well as an evaluation metric describing its accuracy,
which should be defined well enough to represent numerous operational and
quality-related criteria, including geometric alignment. Expectations need to
be adjusted, considering the type of the materials involved, along with the
properties of the robot and the gripper. The evaluation metric standing for
the reliability of a certain pose estimation instance could be defined based
on the conditional probability of successfully completing the task given the
current pose [78].

###  III-C Compliance

Tools and machines with high positioning accuracy are relatively expensive,
which rises associated costs, especially in the case of RA. Therefore,
_compliance_ , i.e. _the relationships between the motions and forces produced
by the robotic manipulator and the part at their contacting point_ , is of
paramount importance.
Compliance plays a crucial role in applications where the execution of the
assembly task requires the presence of certain motion or force profiles, and
in contexts where the positioning requirements result in relatively small
error tolerances due to e.g. the uncertainties involved.
Compliance may also greatly contribute to avoiding jamming and wedging, as
well as to achieving robustness against external motions and forces affecting
the robotic EE131313The device attached to the end of a robotic arm, which is
supposed to perform the main task the robot is aimed at, and interact with the
work environment [148, 149]., and performing delicate tasks such as window-
wiping, which demand both position and force control.
Compliance may be modelled using stiffness or compliance matrices, and
accomplished through the robotic joints’ servo-actuators based on their
stiffness, which may be controlled indirectly by manipulating the joint
torques. Doing so for achieving feedback control of the robotic EE’s force or
position, or both, constitutes _active compliance_.
The limited stiffness of the parts being assembled and numerous elements of
the robotic manipulator, including the gripper, the drive system and the
links, may further contribute to the compliant motion, which is referred to as
robot _passive compliance_.
Both active and passive compliance have advantages and disadvantages, which
need to be taken into account, considering the particularities of the certain
RAD task at hand, in order to devise and implement a suitable compliance
strategy [150].
For example, as shown in Fig. 6, from the point of view of a 2D cross-section,
a cylindrical peg that is being withdrawn from a clearance-fit hole may be in
four possible statuses in terms of the corresponding contacts: no contact,
single-point, two-point or line. A two-point contact may lead to wedging or
jamming. This is an instance of the cases where active-compliance control
could play a significant role. The degree, location and initial error related
to the compliance considerably affect the resulting performance. A quasi-
static analysis of the foregoing factors, as well as the range, position and
boundary conditions of the two-point contact region has been reported in [79].

Figure 6: Schematic illustration of a peg being withdrawn from a hole in a
disassembly process, involving (a) Zero, (b) One, (c) Two or (d) Infinite
contacting points between them, the latter constituting a line. The figure has
been taken from [79].

###  III-D Tackling Uncertainties

When automation is utilized as the main strategy for improving the
repeatability of RAD processes, inherent uncertainties and errors can
undermine performance [80]. Robotic disassembly may be complicated by various
types of uncertainties, e.g. components interlocking, which requires making
use of re-planning strategies enabling to detect separable components and
subassemblies. The foregoing aim could be achieved in a minimal number of
steps through checking for interferences by means of a summation operator and
a list with two pointers, followed by optimization for coming up with new
subassembly directions and sequences [36].
On the other hand, one could think of utilizing interval programming, as well
as fuzzy or stochastic methods, for tackling uncertainties [12].
A considerable part of the relevant literature has been dedicated to
automatically detecting, or even predicting, and then correcting possible
errors while performing [4]RA. Functional Principal Component Analysis could
be used to find out the multi-dimensional feature values associated with force
and torque, followed by clustering the results, by means of e.g. the k-means
algorithm and decision trees, into either successful or one of the several
possible cases of erroneous operation [81].
In the presence of uncertainties, achieving smooth performance in terms of the
tasks allocated to each robot for Multi-Robot stations, modeling a stochastic
partial Assembly/Disassembly Line Balancing Problem and solving it as through
Multi-Objective Optimization could be considered [41].
On the other hand, various [4]RAD tasks may be prone to mechanical errors and
inaccuracies, which need to be simulated, accounted for and compensated. For
example, orbital drilling processes referred to as helical milling, which are
used in aircraft assembly, are susceptible to undesirable performance due to
excessive cutting forces and temperatures. Depending on the types of
uncertainties involved, different offline compensation measures may be taken.
For example, imprecise hole diameters may be alleviated through applying
variable eccentricities at different depths throughout the hole [73].
Auto-calibration for improved reliability might be necessary in cases where
the mechanical design entails changes of physical variables throughout
operations. It may be realized through monitoring the performance by means of
one or more cameras or other types of sensors. For example, kinematic
uncertainties involved in the performance of redundant Cable-drive Parallel
Robots, e.g. the ratio between the actuator angle and the cable length, could
be alleviated through using the measurements returned by an encoder, based on
a model describing the relationships between the measurement errors and
structural parameters [82].

###  III-E Simulating Execution

Simulation of [4]RAD execution can improve predicting and preparing for
uncertain conditions and efficiency [84].
Various pieces of software have been developed for performing robotic
simulations, which is vital for verifying the feasibility of planned [4]AS.
One example is the Box2D physics simulator [58]. Blender and SolidWorks are
instances of relevant software which could be used for 3D modeling. Virtual
assembly and scene reconstruction could be performed using EON Studio [21].
This category of software utilized for designing architectures is referred to
as Computer-Aided Architectural Design. Teamcenter and Vis MockUp are other
pieces of software that could be used for 3D visual simulation in the context
of robotic assembly [22].
Gazebo provides a simulation environment which could be utilized for training
for [4]ASP [60]. RobotStudio could be used for simulating [4]RA operations
[76].
SAMCEF is suitable for Finite Element Analysis141414Analyzing problems using
the finite element method, which solves differential equations numerically in
two or three state or boundary variables [151, 152]. of assembly tasks such as
drilling, in order to predict and account for possible uncertainties [73].
The Industrial Path Solutions software [83] is a suitable choice for
developing virtual models of operations environments, especially when it comes
to analyzing the application of [4]HRC [84].

###  III-F Human-Robot Collaboration

Although [4]RAD may be extremely efficient in handling repetitive, routine,
strategic or general tasks with prescribed forces and relatively high spatial
precision and power, it may not be flexible enough to handle changing task
requirements, complicated jobs, non-serialized products or large parts. These
are requirements for fast and low-cost production, and should be made
symbiotic, immersive, dynamic, fast and safe to be attractive for industrial
parties, who could utilize such systems with strong control interfaces [93,
85].
This motivates [4]HRC for [4]RAD, which requires certain considerations in
terms of [4]SP for the satisfaction of requirements such as a suitable
workload balance [37]. In fact, the main advantages of a human operator
collaborating with a robot in the context of assembly or disassembly arise
from the intelligence and dexterity brought by the human, which combined with
the aforementioned virtues of robots, make [4]HRC systems greatly flexible,
accurate and reliable. Nevertheless, allocating and balancing the tasks based
on the available skills and a classification of the tasks based on the level
of complexity [57] for achieving the best possible performance gives rise to
an additional Process Planning challenge [86].
In the presence of various types of uncertainties, e.g. in the context of
disassembling press-fitted products, human-robot collaboration takes essential
importance, and could be realized through active compliance control [87]. It
is substantially useful for improving the flexibility, efficiency and
applicability of robotic disassembly, as well as alleviating the idle time and
the manual burden on the human collaborating with the robot(s), due to the
further intelligence and assistance brought by human-robot collaboration
through the knowledge base made using Natural Language
Processing151515Programming computers to understand concepts, as well as the
nuances associated with the context, from large volumes of documents
representing natural human language, as well as organizing and categorizing
them [88, 153]. from disassembly relationships, provided as a robotic
knowledge graph, which is helpful for obtaining, analyzing and managing the
disassembly data [89].
It should be noted that one of the factors that could affect the accuracy of
[4]HRC for [4]RAD tasks is human fatigue, which more frequently occurs during
processes of disassembling products of the same type, and needs to be duly
modeled and taken care of [90]. Moreover, the workers’ level of experience and
proficiency, as well as structural similarities between different products,
may have an impact on the operational requirements and limitations [47].
Brain-Computer Interfaces may also be utilized for enhancing the communication
between the human and the robot. It leads to higher flexibility in handling
situations where the workspace of the robot has been largely affected by
external constraints or where the operator needs extra external assistance
from another human operator. This can be achieved by using wavelet
decomposition for extracting certain sub-bands of ElectroEncephaloGraphy (EEG)
signals which may be characterized based on the underlying frequency, e.g. 0–4
Hz or 4–8 Hz. The Pearson correlation coefficient could be utilized to find
out the relation between the 14 pairs of EEG channels. The validity of brain
network parameters provide an indication of the motion characteristics. The
eye movement characteristics could be analyzed based on the information
provided through certain channels. Using the foregoing strategy, it is
possible to detect intended left and right motions with an accuracy of about
97%, and improve the efficiency by about 60% compared to the case where
another human operator performs the task of assistance, instead of an
auxiliary robot [91].
Since human operators are always prone to various types of errors caused by
e.g. fatigue, their safety constitutes a major concern in the design of [4]HRC
for [4]RAD systems. This could be analyzed based on a formal non-deterministic
model. In this way, a safety engineer could detect all possible cases of
dangerous, erroneous human actions, and revise the design over multiple
iterations, until all of them are detected and accounted for [92].
Other measures intending to improve the safety in the context of [4]HRC
consider monitored regions according to which the motions of the robot could
be constrained, as well as utilizing safety skins [64].
Developing a reconfigurable, continuous co-share environment for [4]HRC may
benefit from software decision mechanisms, redundant transmission protocols
and management frameworks for unifying various resources of sensing [93].
On top of safety, for several other reasons, it is vital to equip robots used
in [4]HRC with cognitive skills utilizing hybrid conditional planning. Mainly,
the robot should be capable of handling high-level planning for [4]ASP and
[4]DSP, as well as geometric feasibility checks. Moreover, the robot needs to
be capable of performing communication, sensing and commonsense reasoning
[62].
Operator task allocation is an essential component of the process of
Cooperative Assembly/Disassembly Sequence and Task Planning, which determine
the task sequence of each operator, as well as the time taken to perform each
sequence. They could be performed using Genetic Algorithms, based on the
associated Disassembly Hybrid Graph Model [32].
A major benefit of [4]HRC arises from the opportunity to use remote lead-
through control strategies based on a Cyber-Physical System161616A system in
which a mechanism is monitored or controlled using computer software [154].,
possibly using a model-based display, which obviates the necessity of a human
operator being physically present in a hazardous operational site, such as
dusty, high-pressure or high-temperature environments [94]. For example, in
the context of coating industry, the application of a lag-removing robot for
zinc pot on a hot-dip galvanizing line has been reported in [95].
[4]HRC systems employed in [4]RAD applications may take substantial advantage
of gesture recognition technologies for a more smooth and natural performance,
through which the human operator guides the robot throughout the course of
performing the relevant tasks. A gesture recognition framework requiring a
single shot has been proposed and tested on the JiaJia social robot in [96],
where Dynamic Time Warping is utilized for temporally aligning two sequences
of gestures based on a frame-specific indicator of distance representing
spatial features. Moreover, attention regions are extracted in a two-
directional manner, which allows to retrieve the necessary data from both
static and dynamic, i.e. hold and movement, parts of each gesture.
Continuous online recognition of gestures presented by multiple users may lead
to improved robustness, compared to cases where a separate database is used
for each user. The sensory information could be obtained using non-interfering
equipment, e.g. sensors mounted on the tools and depth cameras, which has been
realized with precision and recall rates of about 90% in [97]. In the
foregoing context, the choice of features and focusing on the most effective
ones, which are usually concentrated on hand movements, may play a major role
in achieving a desirable recognition performance.
Force control, on the other hand, plays an essential role in [4]HRC-based
[4]RAD frameworks, where the traction force inserted by the hand and the
contact force between the manipulated object and the End-Effector are captured
using six-dimensional sensors, e.g. tandem type ones, followed by interpreting
the data in order to determine the motion intended as a part of the guidance
[98].
Dynamic hybrid position/force controllers are useful for performing [4]RA
tasks with Multi Degrees of Freedom manipulators, which may be redundant,
modeling the constraints on the End-Effector as sets of hypersurfaces [99].
On top of force control, other communication modalities such as vision systems
aimed at detecting and tracking objects and air-pressure sensors, as well as
the ones provided through wearable devices, including Augmented Reality
technologies, haptic interfaces and audio feedback, may contribute to the ease
of [4]HRC for [4]RAD [64].

####  III-F1 Learning from Human Demonstrations

It is possible to make a robot learn Pick-and-Place171717Fast, high-precision
placement of a wide range of electronic [4]SMD, including integrated circuits,
resistors and capacitors, as well as through-hole components, onto [4]PCB
utilized in telecommunication, medical, military, automotive and industrial
devices, consumer electronics and computers [155]. policies to be employed for
assembling a product using a demonstration of its disassembly. This may help
facilitate the task for non-expert human users who do not find engaging in
computer programming convenient. The foregoing procedure requires making use
of task-tailored training or 3D Computer Aided Design models for pose and
orientation estimation. Enabling the robot to generalize to other objects
could be realized through shape matching processes aimed at learning shape
description priors, which visually represents the geometric correspondence
between the surface of the object and that of its target location. The
foregoing functionality could be achieved in a self-supervised manner, and
allows to obtain a more widely applicable perception of the task in terms of
how parts of different shapes and associated surfaces fit together in the
context of assembly [61].

Figure 7: System diagram: expert mode allows the expert operators to introduce
their knowledge into the system. Process mining automatically generates the
assembly models from the operation logs, and training allows the new operators
to learn the assembly [24].

Learning by demonstration techniques, such as Task Parametrized Gaussian
Mixture Model, contribute to leveraging the complexity and time-consumption
involved in programming for the trajectory generation and execution of [4]RA
operations. The demonstrations could be captured using a motion sensor such as
Kinect, and then supplied into the training module using a probabilistic
model. Subsequently, Petri Nets181818A family of discrete dynamic systems
utilized for mathematical modeling of distributed systems, where places and
transitions are shown as white circles and rectangles, respectively, within a
bipartite graph [156]. could be utilized to automatically generate assembly
plans for the parts detected from the sensory data [76].
In order to reduce the number of demonstrations that are required for training
the robot to perform a certain task, it is possible to use neural dynamics. In
this case, a single demonstration may suffice for collecting the necessary
information regarding the underlying sequence of sub-goals and the time
intervals between them. The robot could, in turn, instruct non-expert human
operators based on what it has learned about the sequence of tasks, with an
adjustable time scale [63].
Finally, Virtual Reality could be utilized to enable expert users to transfer
their knowledge and experience to other operators through process mining [24].
This process is illustrated in Fig. 7.

##  IV Facilitating Planning and Execution by Design

Assembling a product comprising of $N$ parts typically consists of $N-1$
steps. Reducing the number of parts by design improves efficiency and reduces
costs [68].
Discrete elements may contribute to developing modular ultralight lattice
structures, which facilitate the manufacturing and [4]RA of elements.
Furthemore, they can be possibly further expanded, disassembled or reassembled
to larger structures of various types. The resulting structure will also be of
improved navigability by small mobile robots, as well as enhanced structural
efficiency, where the wasted payload volume could be minimized as a result of
the upgraded packing efficiency. It should be noted that these properties are
affected by geometric variables such as the face, edge and vertex connectivity
between the unit cells, along with the density of the attachments over a unit
of area or a given number of voxels. In the following, we briefly discuss
desirable mechanical properties of resulting structures [157].

####  IV-1 Stiffness and Strength Scaling

The power by which stiffness and strength scale with respect to the density,
i.e. the fraction of the space being occupied by the solid part of the
material [158], are of paramount importance in determining the extent to which
it may comply with the requirements of an [4]RAD framework. Such properties
are affected considerably by the cell wall bending of the cellular structure,
and may be evaluated using dimensional analysis in conjunction with beam
theory. Certain geometric shapes have been studied in terms of the foregoing
scaling in the literature, and those that are not may be, at least roughly,
evaluated based on the approach introduced in [159], which relates the
foregoing properties of lattice structures to the connectivity of the unit
cells, depending on its microstructural characteristics being either axial
stretch dominated or transverse.

####  IV-2 Assembly Efficiency

An efficient [4]RA system needs to be fast and low-cost. Moreover, the
tolerance required seriously affects the resulting efficiency. Furthermore,
assuming that a standardized manner of utilizing fasteners exists and has been
adopted, the number of fasteners required for performing [4]RA is also
considered an important factor in assessing the efficiency of the overall
system.

####  IV-3 Tiling

Noticing its effect on the level of complexity of the required robot
locomotion to move across the structure, the packing strategy considered for
the unit cells that have been fastened to each other, i.e. the spatial
relationship between the unit cells, also becomes a major factor in
determining the efficiency. As illustrated in Fig. 8, options include Simple
Cubic packing, which stands for the consecutive layers of the unit cells
laying directly on top of each other, and Body Centered Cubix tiling,
referring to alternating layers sitting offset to each other.

Figure 8: Schematic illustration of various packing and tiling strategies for
unit cells that have been fastened to each other. In the above geometric
configurations, the attachments re shown in gray, where an adjacency is
defined a neighboring voxel’s location with respect to the origin voxel, and
an attachment stands for a fastener’s location. The sub-figures to the top-
left and bottom-right represent _Simple Cubic_ packing, while the ones to the
top-right and bottom-left illustrate _Body Centered Cubic_ tiling. The figure
has been taken from [157].

####  IV-4 Packing Efficiency

The packing efficiency may be formulated as the volume of completed lattice,
i.e. deployed volume, to un-assembled voxels’ payload volume.

####  IV-5 Volume Allowance for Robotic End-Effector

As shown in Fig. 9, the ratio of the largest clearance volume through which
the robotic [4]EE may enter or exit a unit cell to the whole volume of the
cell, which is referred to as volume allowance, and determines the level of
accessibility of the corresponding attachments, is an essential indicator of
the efficiency, in that it is vital for providing the robot with a range of
motion being large enough to enable it to maneuver and reach the unit cells
and attach them to each other.

Figure 9: Illustration of the clearance volume, which represents the greatest
volume within which the robot’s [4]EE may enter or exit a cell for assembly.
It’s ratio to the total volume of the cell is referred to as volume allowance.
It is shown in gray, for different unit cells. Arrows show the direction of
motion. Figure taken from [157].

####  IV-6 Strut Clearance Angle

Another parameter affecting the clearance provided for the robotic [4]EE to
operate near a node is the strut clearance angle, which is defined as the
angle between the adjacent strut and the vector orthogonal to the node.

Holding and aligning fixtures is an essential part of every [4]RA and Pick-
and-Place procedure. Manual design of fixtures may practically take numerous
iterations, even for professional designers, which motivates automatic design
of cut-outs based on parametrized imprints through dynamic simulation
platforms.

##  V Sustainability and Circular Economy Considerations

We have provided a review of characteristics of [4]RAD practices in re-
manufacturing and recycling processes, as well as a review of their related
design and operational challenges and corresponding problems. As CE and
resource conservation serve as one of the major drivers behind re-
manufacturing processes, it would be essential to establish the net
contributions, i.e. benefits versus impacts, of [4]RAD applications. In this
sense, there is a need to assess economic, environmental, and social benefits
of [4]RAD processes in re-manufacturing and recycling, and integration of
these benefits into a sustainability assessment framework, as a means of
performance analysis, using multiple criteria analysis [56] or integrated
performance matrix-based [160] approaches.
From an economic perspective, the demand for re-manufactured products and
components is increasing. To stay competitive, the re-manufacturing firm has
to offer appealing prices which requires stringent measures to reduce cost.
Unfortunately, the complexity of disassembly processes in dealing with
multiple steps and varying assembly configurations could result in extended
disassembly time as well as increased costs [160]. As such, there is a need to
industry benchmarks as to what level of complexity versus cost corresponds to
a break-even point for adoption of [4]RD applications, in which an economic
gain, i.e. profitability, is still foreseeable for [4]RD-based re-
manufacturing [161]. This margin of profit could be leveraged to justify a
partial transfer of the responsibility to manufacturers in accepting
additional costs for modifications in design and production process to
facilitate disassembly [162]. In the long term, the performance and quality of
[4]RD practices could also be subject to compromises due to degradation in
reliability, as well as the precision of robots, and resulting disassembly
errors and disruptions, requiring a service life asset management approach for
robots and their components [163, 164]. Further, the economic viability of
[4]RD applications relies on their economy-of-scale, i.e. scale-ability, and
their robustness and applicability to a diverse pool of used products and
assemblies [165]. In this regard, there is a need to develop Key Performance
Indicators to trace and analyze the economic efficiency of RD practices over-
time subject to changes in configurations, volume, and quality of assemblies.
Such indicators could include the ratio of manual to automated disassembly
time, ratio of operations requiring tool-change, unit disassembly time,
marginal cost of disassembly, and disassembly efforts index [165, 56].
With respect to environmental benefits, RD practices face challenges in terms
of maintaining and improving their performance in reuse of disassembled
components, i.e. re-manufacturing, recovery of materials, i.e. recycling, and
proper segregation of hazardous materials for disposal, due to increasing
quantity and diversity of used products, and their varying end-of-life
qualities [166, 56]. The carbon emission inventory of [4]RAD practices shall
be a part of the performance assessment measures, as the collection,
transport, sorting, and processing of used products results in carbon
emission. More precisely, [4]RD processes could be energy intensive due to
direct use of energy, e.g. melting and recovery of metals from electronics
boards, or extensive repetition of mechanical operations [167]. In this
regard, mass recovery ratio, emission rate per unit of disassembly, and energy
intensity per unit recovery have been suggested as examples of environmental
performance assessment of [4]RD practices [56, 166]. In this regard, the
environmental performance can only be accurately assessed by adopting a life
cycle analysis approach considering the direct and embodied emission
inventories resulted from manufacturing of robots and their parts alongside
the inventories or savings in [4]RD processes [168, 169].
[4]RAD practices for re-manufacturing are a host of several social and
societal challenges. There are various approaches in measurement and
assessment of social performance of production processes, including
Stakeholders’ theory, institutional theory and resource-based view, socio-
technical transition theory, among others [170]. Quality control and
compliance with customers’, and workers’, health and safety requires adoption
of specific procedures and benchmarks given the lack of industry wide
standards [171]. There exist a trade-off between product customization and
capital, as well as operational cost of [4]RAD. Flexibility in [4]RAD
processes for re-manufacturing is an essential attribute to promote users’
appreciation and generate steady demand. Provided the capital intensity of
robotic assembly and disassembly lines and setups, many of such processes are
task-based and have limited robotic capacities to support customization and
diversification of products [165]. In case of literature on human-robot
interaction, the focus has been on human factors considerations in design and
operation of robotic processes. In case of [4]RAD for re-manufacturing, the
working environment is more prone to exposing workers to toxic and hazardous
materials arising from recycling [172, 173] and poorer workplace air quality
due to released particulate matters during [4]RD process, as well as risks of
accidents and injuries due to (unexpected) robot motions [174]. These
conditions could affect the performance of workers and hinder the quality of
their interactions with robots [171]. In the long term, the main societal
benefits of [4]RAD include increased landfill diversion, resource, i.e.
material, conservation and efficiency by promoting automation, precision, and
standardization of re-manufacturing processes, as well as creation of local
jobs related to collection, sorting, and monitoring of re-manufacturing
process. As such, the [4]RAD practices are in need of developing indicators to
assess and report on their performance in line with social dimensions. These
indicators could be risk-based, such as the likelihood of workers’ exposure to
hazardous substances per person per re-manufactured product [172, 173]. There
are also impact-based indicators presenting the social and societal benefits
of the [4]RAD-based re-manufacturing, such as the amount of landfill diversion
per [4]RAD process per year [166].

##  VI Conclusions and Future Work

Integrating the whole [4]RAD pipeline into an end-to-end system such as click-
and-assemble [4]RA [175] is a promising direction. It should enable the user
to operate the system remotely using solely a Computer Aided Design file,
where the necessary calculations and preparations for e.g. [4]ASP are
performed automatically.
The fourth industrial revolution has benefited from various aspects of
traditional and industrial manufacturing platforms, machinery functionalities
and practices, along with the advantages brought by recent smart technologies,
altogether forming what is referred to as _smart manufacturing_.
Ideally, such a _smart_ system should be capable of self-diagnosing the
problems, and perform self-monitoring, thereby obviating the necessity of the
presence of a human operator, and enhance the level of automation through more
efficient data exchange strategies made possible by means of [4]AI, cognitive
computing, cloud computing, industrial Internet-of-Things and Cyber-Physical
Systems (CPS). Internet-of-Things191919Enabling computers to obtain useful
data from, and about, the environment, i.e. ‘things’, without requiring human
aid, thereby making it possible to acquire information more accurately,
rigorously and extensively [176]. for manufacturing, Internet-of-
Everything202020Further involving other elements, such as people, data and
processes, resulting in a more comprehensive concept than the Internet-of-
Things [177]., Manufacturing 4.0, smart factory, connected enterprise and
industrial internet often indicate a similar, if not the same, concept.
A smart factory resembling Industry 4.0 employs a modular structure, where a
CPS monitors physical processes, and decisions are made in a decentralized
manner. In the foregoing context, as participants of a value chain, CPSs
communicate and collaborate with each other, as well as with human beings,
both within ad across organizations, over the Internet-of-Things.
There are a variety of notions which could potentially open further windows to
more highly reliable platforms in the context of Industry 4.0. For example,
such frameworks could be equipped with a capability of performing predictive
maintenance, which would prevent anticipated losses or damages due to e.g.
machinery failures. On the other hand, if realized, since the system utilizes
the Internet-of-Things, appropriate use and processing of the data returned by
the relevant sensors could enable various organizations participating in the
value chain help each other to detect and alleviate their problems in a more
cost-effective way through e.g. predicting and notifying other participants of
their expected faults and failures.
One of the technologies being supposed to play a major role in the advancement
of Industry 4.0 is _3D printing_ , which is widely believed to facilitate the
design process, due to its versatility and flexibility in printing a wide
range of geometric structures. Moreover, it is environmentally fairly
friendly, and especially for smaller production platforms reduces the lead
time, as well as the average production cost. Furthermore, it decreases the
warehousing expenses, and helps enable mass customization. Last but not least,
3D printing could make it possible to print spare parts locally, which may
exempt a user from ordering and waiting for the parts to be delivered and
installed by the provider [178, 179].
Assembling complex and customized products incentivizes the development of
systems involving actors of various types, which need to cooperate with each
other through e.g. an intelligent CPS-based could framework. It is worth
noticing that with increasing contributions of AI to [4]RAD, it is expected
that sensory information acquired from the real production environment will
enrich the assembly or disassembly knowledge model, helping search the
available instance library to detect a similar product structure, and guide
the process based on the prescribed priority rules [114].
Furthermore, one possible direction for future research could involve trying
to develop strategies for estimating the required time for [4]RAD of given
products automatically [49].
The self-reconfiguring cleaning robots discussed throughout the paper could be
extended to various other applications as well. For example, the robot aimed
at cleaning solar panels could be extended and used for floating solar panels,
solar rooftops and solar farms [180].
Regarding Knowledge Transfer, representations of assemblies involved in the
present topology might not generalize to actual modular products or LEGO
block-based 3D structures involving different practical requirements arising
from e.g. non-perpendicular angles between the profiles or nonparallel
surfaces. Thus the parts, as well as the relevant restriction, need to be
interpreted in terms of surfaces, along with the relationships between them
[70].
In fact, some of the most important advantages of RAD in Industry 4.0 arise
from their contributions to CE, which is essential for climate-change
considerations and sustainable development. Further development over various
aspects of automatic or semi-automatic CE-targeted RAD solutions, including
Human-Robot Collaboration (HRC) and Assembly/Disassembly SP, is left upon
future research for both academic and industrial parties.
Nevertheless, not all countries engaged in robotic-related research have yet
started paying attention to the technological development of RAD. RD requires
even more serious attention. Despite the prevalent conception, in reality,
simply reversing the associated RA process may not be feasible due to e.g.
geometric constraints or joint specifications, which makes the importance of
Design for Disassembly stand out, among other considerations [181].
One of the concepts requiring further attention in future studies is a
systematic review of the existing robotic technologies, as regards their
applicability to, and suitability for, specific RAD applications. More
clearly, the efficiency of a RAD process depends, among other factors, on the
effectiveness of the robot in performing the tasks planned, which,
hypothetically, can be taken into account and optimized at the same time as
the rest of the components of the whole plan. To the authors’ knowledge, this
is widely missing from the existing literature, requiring more serious
attention to it in the relevant future studies.

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