This paper addresses security challenges in multi-robot 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.

翻译：本文针对多机器人系统中因敌方可能劫持机器人控制权而引发的安全挑战（如未经授权进入禁区的风险），提出了一种融合相互观测机制与可达性约束的新型多机器人最优规划算法。该算法确保即使存在敌方运动干预，被劫持机器人若错过预定协同观测任务仍无法突破禁区边界。可达性约束采用椭球过逼近方法实现高效的冲突检测与梯度计算。为提升系统鲁棒性并解决可行性问题，我们引入了子团队机制：用具有内聚性的子团队替代单一路径上的个体机器人分配，允许冗余机器人脱离原轨迹执行跨路径协同观测，使得多子团队的安全保障无需调整原有规划。通过将原始无安全防护的多机器人轨迹生成检查点图，并求解网络流覆盖问题，我们构建了跨轨迹协同观测方案，为抗计划偏离攻击提供等同的安全保障。实验证明，所提算法能显著增强多机器人系统面对敌方威胁时的安全效能与鲁棒性。