2023/05/26 by Ehsan Latif, Latif, Ehsan, Wen‐Zhan Song +3 · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence #Multiagent Systems (cs.MA) #Networking and Internet Architecture (cs.NI) #Robotics (cs.RO) #Slime Mold and Myxomycetes Research
paper · pdf · doi:10.48550/arxiv.2305.17087
openalex publication_date 2023/05/26 · openalex created_date 2023/05/30 · openalex updated_date 2026/07/28
Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots. It is made efficient by hierarchically building on the use of local information exchanges. We consider a case study application of maze solving through cooperation among a group of robots, where the time and costs are minimized while avoiding inter-robot collisions and path overlaps during exploration. With a solid theoretical basis, we extensively analyze the algorithm with realistic CORE network simulations and evaluate it against state-of-the-art solutions in terms of maze coverage percentage and efficiency under communication-degraded environments. The results demonstrate significantly higher coverage accuracy and efficiency while reducing costs and overlaps even in high packet loss and low communication range scenarios.