Typical cooperative multi-agent systems (MASs) exchange information to coordinate their motion in proximity-based control consensus schemes to complete a common objective. However, in the event of faults or cyber attacks to on-board positioning sensors of agents, global control performance may be compromised resulting in a hijacking of the entire MAS. For systems that operate in unknown or landmark-free environments (e.g., open terrain, sea, or air) and also beyond range/proximity sensing of nearby agents, compromised agents lose localization capabilities. To maintain resilience in these scenarios, we propose a method to recover compromised agents by utilizing Received Signal Strength Indication (RSSI) from nearby agents (i.e., mobile landmarks) to provide reliable position measurements for localization. To minimize estimation error: i) a multilateration scheme is proposed to leverage RSSI and position information received from neighboring agents as mobile landmarks and ii) a Kalman filtering method adaptively updates the unknown RSSI-based position measurement covariance matrix at runtime that is robust to unreliable state estimates. The proposed framework is demonstrated with simulations on MAS formations in the presence of faults and cyber attacks to on-board position sensors.

翻译：典型的多智能体协同系统（MAS）通过信息交互，在基于邻近性的控制一致性框架中协调运动以完成共同目标。然而，当智能体机载定位传感器发生故障或遭受网络攻击时，全局控制性能可能受损，导致整个MAS被劫持。对于在未知或无地标环境（如开阔地形、海洋或空中）中运行，且超出邻近智能体感知范围/距离的系统，受损智能体将丧失定位能力。为保持此类场景下的弹性，我们提出一种利用邻近智能体（即移动地标）的接收信号强度指示（RSSI）为受损智能体恢复定位能力的方法，从而提供可靠的位置测量信息。为最小化估计误差：i) 提出一种多边定位方案，利用来自邻近智能体（作为移动地标）的RSSI和位置信息；ii) 采用卡尔曼滤波方法，在运行时自适应更新基于RSSI的未知位置测量协方差矩阵，该方法对不可靠的状态估计具有鲁棒性。通过在编队MAS中模拟机载位置传感器故障及网络攻击场景，验证了所提框架的有效性。