Solving a Multi-robot Search Problem with Bionic Sarsa Algorithm and Artificial Potential Field

Safe and effective path planning of multiple combat vehicles engaged in antagonistic environments keeps a challenging problem. Based on the application background of multi-robots in cooperative reconnaissance of enemy camps in environment with traps, this paper studies the multi-agent path planning based on bionic algorithms and artificial potential field method. The proposed bionic PP-AP Sarsa Scheme is inspired by food-finding scheme of Physarum Polycephalum (PP), which can effectively solve the dimensional explosion problem of traditional multi-agent reinforcement learning methods. This paper first studies the single-agent bionic planning problem with the PP algorithm to initialize the Q table used in Sarsa-based reinforcement learning, which effectively reduces the search space and accelerates the convergence speed of the early stage of reinforcement learning. After the Q tables in the same map are obtained through the training of different single agents, the Q tables of every agents are extended to multi-agents scenario by the assistance of simplified artificial potential field, hence a composite parallel path planner named RL-APCP³ is constructed to synchronously update the actions of all of the agents, which allows us to complete the coordinated and efficient search of enemy camps by multiple agents. Compared with the Sarsa path planning algorithm of single agent, the efficiency of this scheme is improved up to 55.22%.