Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage

In this work, we develop a novel decentralized coordination algorithm for a team of autonomous unmanned aerial vehicles (UAVs) to surveil an aggressive forest wildfire. For dangerous environmental processes that occur over very large areas, like forest wildfires, multi-agent systems cannot rely on long-range communication networks. Therefore, our framework is formulated for very restrictive communication constraints: UAVs are only able to communicate when they are physically close to each other. To accommodate this constraint, the UAVs schedule a time and place to meet in the future to guarantee that they will be able to meet up again and share their belief of the wildfire state. In contrast with prior work, we allow for a discrete time, discrete space Markov model with a large state space as well as restrictive communication constraints. We demonstrate the effectiveness of our approach using simulations of a wildfire model that has 10
²⁹⁸
total states.