Multi-Agent Trajectory Optimization Against Plan-Deviation Attacks using Co-Observations and Reachability Constraints

In this paper, we focus on using path planning and inter-agent measurements to improve the security of multi-robot systems against possible takeovers from cyber-attackers. We build upon recent trajectory optimization approaches where introspective measurement capabilities of the robots are used in an co-observation schedule to detect deviations from the preordained routes. This paper proposes additional constraints that can be incorporated in the previous trajectory optimization algorithm based on Alternating Direction Method of Multipliers (ADMM). The new constraints provide guarantees that a compromised robot cannot reach a designed safety zone between observations despite adversarial movement by the attacker. We provide a simulation showcasing the new components of the formulation in a multi-agent map exploration task with several safety zones.