A decision-theoretic planning approach for multi-robot exploration and event search

Event exploration is the process of exploring a topologically known environment to gather information about dynamic events in this environment. Using multi-robot systems for event exploration brings major challenges such as finding and communicating relevant information. This paper presents a solution to these challenges in the form of a distributed decision-theoretic model called MAPING ( Multi-Agent Planning for INformation Gathering), in which each agent computes a communication and an exploration strategy by assessing the relevance of an observation for another agent. The agents use an extended belief state that contains not only their own beliefs but also approximations of other agents' beliefs. MAPING includes a forgetting mechanism to ensure that the event-exploration remains open-ended. To overcome the resolution complexity due to the extended belief state we use a method based on the well-known adopted assumption of variables independence. We evaluate our approach on different event exploration problems with varying complexity. The experimental results on simulation show the effectiveness of MAPING, its ability to scale up and its ability to face real-word applications.