Informative Path Planning for Persistent Surveillance through Information Sharing between Heterogeneous Agents

Using many agents with different characteristics is more effective than using a homogeneous agent to observe a vast environment persistently. In order for heterogeneous multi-agents to efficiently perform persistent observation of a changing environment, it is necessary to plan the most informative paths for each agent according to its observation capability. In this paper, we report the results of applying the path planning method for maximizing the amount of information obtained by Monte Carlo tree search to the mutual information map obtained from the estimated state of the environment and the observation ability of the agent.