  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.
