Decentralized and Asynchronous Multi-Agent Active Search and Tracking when Targets Outnumber Agents

Published: 27 Oct 2023, Last Modified: 22 Dec 2023RealML-2023EveryoneRevisionsBibTeX
Keywords: Probability Hypothesis Density, Thompson sampling, Decentralized Multi-Agent
Abstract: Multi-agent multi-target tracking has a wide range of applications, including wildlife patrolling, security surveillance or environment monitoring. Such algorithms often assume that agents are pre-assigned to monitor disjoint partitions of the environment, reducing the burden of exploration. This limits applicability when there are fewer agents than targets, since agents are unable to continuously follow the targets in their fields of view. Multi-agent tracking algorithms additionally assume a central controller and synchronous inter-agent communication. Instead, we focus on the setting of decentralized multi-agent, multi-target, simultaneous active search-*and*-tracking with asynchronous inter-agent communication. Our proposed algorithm DecSTER uses a sequential monte carlo implementation of the probability hypothesis density filter for posterior inference combined with Thompson sampling for decentralized multi-agent decision making. We compare different action selection policies, focusing on scenarios where targets outnumber agents. In simulation, DecSTER outperforms baselines in terms of the Optimal Sub-Pattern Assignment (OSPA) metric for different numbers of targets and varying teamsizes.
Submission Number: 42