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

Abstract

This paper addresses security challenges in multirobot systems (MRS) where adversaries may compromise robot control, risking unauthorized access to forbidden areas. We propose a novel multi-robot optimal planning algorithm that integrates mutual observations and introduces reachability constraints for enhanced security. This ensures that, even with adversarial movements, compromised robots cannot breach forbidden regions without missing scheduled co-observations. The reachability constraint uses ellipsoidal over-approximation for efficient intersection checking and gradient computation. To enhance system resilience and tackle feasibility challenges, we also introduce sub-teams. These cohesive units replace individual robot assignments along each route, enabling redundant robots to deviate for co-observations across different trajectories, securing multiple sub-teams without requiring modifications. We formulate the cross-trajectory co-observation plan by solving a network flow coverage problem on the checkpoint graph generated from the original unsecured MRS trajectories, providing the same security guarantees against plan-deviation attacks. We demonstrate the effectiveness and robustness of our proposed algorithm, which significantly strengthens the security of multi-robot systems in the face of adversarial threats.