An Integrated Decision and Control Theoretic Solution to Multi-Agent Co-Operative Search Problems
This addresses the challenge of decentralized coordination for multi-agent systems in scenarios like surveillance or rescue, but it is incremental as it builds on existing frameworks with a novel estimation approach.
The paper tackles the problem of autonomous multi-agent cooperative target search in unknown environments without communication, proposing an integrated decision and control solution that estimates other agents' strategies to generate feasible trajectories. Numerical simulations show the algorithm outperforms random strategies with considerable advantages.
This paper considers the problem of autonomous multi-agent cooperative target search in an unknown environment using a decentralized framework under a no-communication scenario. The targets are considered as static targets and the agents are considered to be homogeneous. The no-communication scenario translates as the agents do not exchange either the information about the environment or their actions among themselves. We propose an integrated decision and control theoretic solution for a search problem which generates feasible agent trajectories. In particular, a perception based algorithm is proposed which allows an agent to estimate the probable strategies of other agents' and to choose a decision based on such estimation. The algorithm shows robustness with respect to the estimation accuracy to a certain degree. The performance of the algorithm is compared with random strategies and numerical simulation shows considerable advantages.