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：典型的合作多智能体系统（MASs）通过基于接近控制一致性方案的信息交换来协调其运动，以完成共同的目标。然而，在出现代理人的机载定位传感器故障或网络攻击的情况下，全局控制性能可能会受到损害，从而导致整个MAS的被劫持。对于在未知或无地标的环境（例如，开阔地形、海洋或空中）以及超出附近智能体的距离/接近传感范围的系统，受损代理失去了定位能力。为了在这些情况下保持弹性，我们提出了一种方法，利用来自附近智能体（即移动地标）的接收信号强度指示（RSSI）提供可靠的位置测量来进行定位，以恢复受损代理。 为了最小化估计误差：i）提出一个多智能体测距方案，利用从相邻代理人作为移动地标接收的RSSI和位置信息，ii）自适应地更新运行时的未知RSSI位置测量协方差矩阵的卡尔曼过滤方法，该方法对不可靠的状态估计具有鲁棒性。在代理人的机载位置传感器存在故障和网络攻击的情况下，通过仿真展示了所提出的框架在MAS形成中的应用。