
In addition, most sensing platforms are battery-powered (e.g., UAVs~\cite{meera2019obstacle}); hence, their lifetime is related to the presence of energy-harvesting sensors, the communication activity, and the processors usage~\cite{sanmiguel2016energy}.
Finally, communication and storage limitations might preclude the possibility to collect (or store) updated information from other robots. In conclusion, limited on-board resources reduce the applicability of MAS schemes and make cooperative algorithms challenging in real-life scenarios. Therefore, future research should focus on the development of accurate models that capture the entire spectrum of resource consumption (e.g., energy, bandwidth, storage) as function of the network activity. This models will allow to apply predictive decision-making strategies and guarantee an optimal balance between resource consumption and network operation.

\subsubsection{Scalability}
Another key issue in multi-platform APE algorithms is the system scalability with respect to the number of platforms. In this regard, decentralized and distributed architectures should be considered; notably, they require agents with minimal on-board processing, communication and storage capabilities, but they do not offer the same optimality guarantees with respect to centralized counterparts~\cite{yang2019survey}.
In mobile robotics, distributed optimization is often used to yield agents coordinated motion. In doing so, each sensing robot locally estimates the utility function to be optimized by aggregating its previous observations with those coming from connected agents; the computational complexity is therefore constant with respect to the number of platforms. This approach is however not cooperative, since each agent only considers its own future actions.  
To enable a higher level of coordination, each platform might consider the effect of other robots actions in the objective estimation process. It incurs a linear computational expense in the number of vehicles, yet the effect of the approximation error is provably reduced from that of the single-node approximation, allowing coupled effects between mobile sensors to be captured. Indeed, field experiments have shown better results using the cooperative approach~\cite{hoffmann2009mobile}.
Robots cooperation can be implemented also via finite state machines~\cite{intermittent2009}. A SAT algorithm can start with the team performing a global search and the next platform action might be chosen by optimizing an objective function that accounts for both individual and collective goals. For instance, it may foster group dispersion over the environment, while discouraging fuel consumption, or it should balance the revisiting of the monitored areas. 
Once a target has been detected, the search and track phase starts: robots perform task assignment (to continue the search or to track the detected target), according to the distance to the target, the current number of platforms focused on that target, and the number of targets that have already been detected~\cite{intermittent2009}.

\subsubsection{Heterogeneity}
Robots heterogeneity stems from diverse sensing and actuating capabilities, computing resources, cognitive algorithms, and  morphology~\cite{rizk2018decision,parker2016multiple}. The literature regarding the utilization of heterogeneous robots has shown the benefits of combining either different types of sensors, different perspectives, or different computational and operational capabilities~\cite{queralta2020collaborative,rizk2019cooperative}. More precisely, parallelisation, specialisation and complementarity are important motivations for developing heterogeneity in multi-robot teams~\cite{esterle2017future}. 
For these reasons, heterogeneity is an increasingly prevalent property in cyber-physical and multi-robot systems~\cite{lewis2014s}


When heterogeneity is related to the perception capabilities, it is referred to as multi-modality, as discussed previously in this Section. 
Multiple sensing modalities capture different properties of the environment; thus, the aggregated multi-modal data allow inferences that might be not possible with single-sensor measurements. 
However, multi-modality represents an important challenge to be handled in heterogeneous multi-sensor applications.
As a matter of fact, different data sources require different calibration procedures, each of which might involve extensive human intervention~\cite{zanella2016best,aghajan2009multi}. Recently, some works have proposed self-supervised calibration procedures requiring minimal human effort. These methods hinge on the correlation between the multi-modal data collected by the platform sensors; in particular, one perception channel is used to produce the supervisory signal for the others, enabling the automatic generation of a labeled dataset~\cite{nava2019learning}. 
Calibration is not the only problem to be tackled when dealing with multi-modal systems. Indeed, different data sources suffer from different types and magnitudes of noise, and they might produce unbalanced data. This, in turn, affects the decision making process, which struggles when data have different quality and resolution characteristics~\cite{khaleghi2013multisensor}. Moreover, unbalanced and missing data might affect the belief map generation procedure~\cite{queralta2020collaborative,khaleghi2013multisensor}. 
Multi-modal data sources might also yield conflicting features. For example, visual sensors and RADARs might detect obstacles at different distances~\cite{kumar2020lidar}. 
All these issues are further complicated when multiple robots are involved: the data to be fused are collected in different physical locations and the sensors move with respect to each other. Thus, it is not trivial to evaluate whether different agents are observing the same objects or not, or how to rank observations from different agents~\cite{queralta2020collaborative}.


From the operational perspective, certain applications (e.g., cooperative localization, surveillance, assistive navigation, exploration and mapping) may require the simultaneous use of robots with actuation and morphological heterogeneity~\cite{chen2021collision}. For instance, smaller platforms have access to narrow passages, but their payload is limited~\cite{chen2021collision}; to solve this issue, it is possible to employ larger robots that carry application-specific payloads (e.g., long-duration batteries, heavy sensors or actuators)~\cite{rizk2018decision}.  
Heterogeneous robotic systems represent a fertile research field, where there are still numerous open questions to be addressed. At first, the wide variety of robots being utilized in APE missions, and the different scenarios in which they can be applied, calls for a certain level of collective and situational awareness. This requires the definition of self-organized swarm intelligence models, which account for robots diversity and take into consideration the surrounding environmental conditions. In this framework, the agents of a heterogeneous multi-robot system should identify the main characteristics and constraints of the current environment, and the conditions of the robots they interact with. 
In particular, heterogeneity-aware APE strategies in large scale MAS scenarios, with variable number and agents capabilities, require each robot to automatically learn the capabilities of their peers.
For instance, a UGV collaborating with other UAVs needs to be aware of the different perspectives that UAVs can bring into the scene, but also of their limitations in terms of operational battery lifetime or payload capabilities. 
In general, heterogeneity-aware algorithms, and the explicit incorporation of agents diversity, can be advantageous for multi-robot decision making processes~\cite{davoodi2020heterogeneity}.
Nevertheless, there is still lack of further research in this area, as most existing projects and systems involving heterogeneous robots predefine the way in which they are meant to cooperate~\cite{queralta2020collaborative}.
This is mainly due to the high complexity associated to the design of heterogeneity-aware APE strategies, where robots are meant to autonomously plan their actions, according to the diverse capabilities of their peers. As a consequence, several works introduce additional simplifications or assumptions, such as the hypothesis that the robots sensing and actuation capabilities are a-priori known over the entire network~\cite{chung2009probabilistic}; others design ad-hoc strategies for a MSMP-APE system where the type and the number of robots is predefined~\cite{dogancay2012uav}. 
These assumptions limit the scalability and the fault-tolerance of the system, since they do not account for the possibility of malfunctioning or missing robots~\cite{maza2007multiple}. 




\begin{table}[b!]
\centering
\resizebox{0\textwidth}{!}{
\begin{tabular}{c ccc c c}
  &  &  &  &  & \\
\end{tabular}}
\end{table}
\section{Platform dynamics}\label{sec:dynamics}

Active sensing can be defined as an intelligent data acquisition process where a feedback is performed on sensory data (raw or processed) to satisfy specific perception performance measures, such as scene exploration~\cite{ergodic_exploration}, target tracking~\cite{ferrari2009geometric}, and object classification~\cite{active_classification}.
According to~\eqref{eq:general_observation_model}, the measurements are affected by the current platform configuration. This implies that any active sensing scheme must include a decision-making process to dynamically modulate the sensing agent behavior through its state~\cite{bajcsy2018revisiting}.
To this aim, the sensing platform is modeled as a dynamical system in most autonomous perception frameworks~\cite{liu2015model}. In particular, the dynamics of sensing platform $m$, defined by its state $\vect{s}_{t,m}$, is usually described with a deterministic Markovian state transition model~\cite{radmard2017active}:
\begin{equation}\label{eq:sensor_dynamics_general}
\begin{split}
    & \vect{s}_{t+1,m} = q_m\left(\vect{s}_{t,m},\vect{u}_{t,m}\right),