Resilient Trajectory Planning in Adversarial Environments
Andrew Clark, Zhouchi Li · 2019
Autonomous systems, including ground and aerial robots, must plan trajectories in order to satisfy performance requirements in uncertain environments. Current trajectory planning approaches do not incorporate resilience to malicious adversaries. We develop a framework for trajectory planning under false data injection attacks, in which a subset of sensors is vulnerable to compromise by an adversary. We propose a two-step control policy, in which the set of feasible control inputs is constrained based on the observations of the non-vulnerable sensors, and then an optimal control is chosen at each time step. We develop a differential dynamic programming algorithm for selecting a nominal trajectory that achieves a desired trade-off between performance and attack resilience, and prove that the chosen trajectory is locally optimal. Our approach is illustrated through numerical study.