Probabilistically Resilient Multi-Robot Informative Path Planning

In this paper, we solve a multi-robot informative path planning (MIPP) task under the influence of uncertain communication and adversarial attackers. The goal is to create a multi-robot system that can learn and unify its knowledge of an unknown environment despite the presence of corrupted robots sharing malicious information. We use a Gaussian Process (GP) to model our unknown environment and define informativeness using the metric of mutual information. The objectives of our MIPP task is to maximize the amount of information collected by the team while maximizing the probability of resilience to attack. Unfortunately, these objectives are at odds especially when exploring large environments which necessitates disconnections between robots. As a result, we impose a probabilistic communication constraint that allows robots to meet intermittently and resiliently share information, and then act to maximize collected information during all other times. To solve our problem, we select meeting locations with the highest probability of resilience and use a sequential greedy algorithm to optimize paths for robots to explore. Finally, we show the validity of our results by comparing the learning ability of well-behaving robots applying resilient vs. non-resilient MIPP algorithms.