An Energy-Efficient Communication Scheme for Multi-robot Coordination Deployed for Search and Rescue Operations

Rajesh, M.; Nagaraja, S. R.

doi:10.1007/978-981-16-1089-9_16

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…We denote the target's capture time as t cap and use t (i) cap to indicate the capture time by robot i, apparently, t cap = min i {t (i) cap }. The MuRES problem is then defined as finding the optimal joint policy π = {π (1) , π (2) , . .…”

Section: A Mures Problem Formulationmentioning

confidence: 99%

“…t ) according to (1), and the pretrained policy parameter (θ i ), the robot executes the policy by choosing a (i) t with the following probability:…”

Section: A Online Decentralized Executionmentioning

confidence: 99%

“…Here, a "nonadversarial" moving target refers to the type of target whose movement dynamics are independent of, and thus do not react to, the searchers' movement strategies. On the one hand, the MuRES problem has many real-world application potentials, such as multirobot search and rescue in hazardous environments [1], [2], [3], [4], [5], collaborative source leakage localization [6], [7], and multirobot security defense and surveillance [8], [9]. On the other hand, MuRES also serves as a representative operation research topic and lies in the intersection of many fundamental research areas, such as multiagent learning [4], [10], [11], game theory [12], swarm dynamics [13], [14], [15], cooperative control [16], [17], and graph theory [18], [19].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Entropy Regularized Policy Gradient for Multirobot Nonadversarial Moving Target Search

et al. 2023

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This article investigates the multirobot efficient search (MuRES) for a nonadversarial moving target problem from the multiagent reinforcement learning (MARL) perspective. MARL is deemed as a promising research field for cooperative multiagent applications. However, one of the main bottlenecks of applying MARL to the MuRES problem is the nonstationarity introduced by multiple learning agents. With learning agents simultaneously updating their policies, the environment cannot be modeled as a stationary Markov decision process, which results in the inapplicability of fundamental reinforcement learning techniques such as deep Q-network and policy gradient (PG). In view of that, we adopt the centralized training and decentralized execution scheme and thereby propose a cross-entropy regularized policy gradient (CE-PG) method to train the learning agents/robots. We let the robots commit to a predetermined policy during execution, collect the trajectories, and then perform centralized training for the corresponding policy improvement. In this way, the nonstationarity problem is overcome, in that the robots do not update their policies during execution. During the centralized training stage, we improve the canonical PG method to consider the interactions among robots by adding a cross-entropy regularization term, which essentially functions to "disperse" the robots in the environment. Extensive simulation results and comparisons with state of the art show CE-PG's superior performance, and we also validate the algorithm with a real multirobot system in an indoor moving target search scenario.

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Section: A Mures Problem Formulationmentioning

confidence: 99%

“…t ) according to (1), and the pretrained policy parameter (θ i ), the robot executes the policy by choosing a (i) t with the following probability:…”

Section: A Online Decentralized Executionmentioning

confidence: 99%