Optimizing Non-Markovian Information Gain Under Physics-Based Communication Constraints

In many exploration scenarios, it is important for robots to efficiently explore new areas and constantly communicate results. Mobile robots inherently couple motion and network topology due to the effects of position on wireless propagation—e.g. distance or obstacles between network nodes. Information gain is a useful measure of exploration. However, finding paths that maximize information gain while preserving communication is challenging due to the non-Markovian nature of information gain, discontinuities in network topology, and zero-reward local optima. We address these challenges through an optimization and sampling-based algorithm. Our algorithm scales to 50% more robots and obtains 2–5 times more information relative to path cost compared to baseline planning approaches.