Topology-Preserving Motion Coordination for Multi-Robot Systems in Adversarial Environments

Abstract

The interaction topology plays a significant role in the distributed motion coordination of multi-robot systems (MRSs) for its noticeable impact on the information flow between robots. However, recent research has revealed that in adversarial environments, the topology can be inferred by external adversaries equipped with advanced sensors, posing severe security risks to MRSs. Therefore, it is of utmost importance to preserve the interaction topology from inference attacks while ensuring the coordination performance. To this end, we propose a topology-preserving motion coordination (TPMC) algorithm that strategically introduces perturbation signals during the coordination process with a compensation design. The major novelty is threefold: i) We focus on the second-order motion coordination model and tackle the coupling issue of the perturbation signals with the unstable state updating process; ii) We develop a general framework for distributed compensation of perturbation signals, strategically addressing the challenge of perturbation accumulation while ensuring precise motion coordination; iii) We derive the convergence conditions and rate characterization to achieve the motion coordination under the TPMC algorithm. Extensive simulations and real-world experiments are conducted to verify the performance of the proposed method.