Emerging applications of collaborative autonomy, such as Multi-Target Tracking, Unknown Map Exploration, and Persistent Surveillance, require robots plan paths to navigate an environment while maximizing the information collected via on-board sensors. In this paper, we consider such information acquisition tasks but in adversarial environments, where attacks may temporarily disable the robots' sensors. We propose the first receding horizon algorithm, aiming for robust and adaptive multi-robot planning against any number of attacks, which we call Resilient Active Information acquisitioN (RAIN). RAIN calls, in an online fashion, a Robust Trajectory Planning (RTP) subroutine which plans attack-robust control inputs over a look-ahead planning horizon. We quantify RTP's performance by bounding its suboptimality. We base our theoretical analysis on notions of curvature introduced in combinatorial optimization. We evaluate RAIN in three information acquisition scenarios: Multi-Target Tracking, Occupancy Grid Mapping, and Persistent Surveillance. The scenarios are simulated in C++ and a Unity-based simulator. In all simulations, RAIN runs in real-time, and exhibits superior performance against a state-of-the-art baseline information acquisition algorithm, even in the presence of a high number of attacks. We also demonstrate RAIN's robustness and effectiveness against varying models of attacks (worst-case and random), as well as, varying replanning rates.
翻译:合作自主的新应用,如多目标跟踪、未知的地图探索和持续监视等,要求机器人计划路径在尽可能扩大通过机载传感器收集的信息的同时,在尽可能扩大通过机载传感器收集的信息的同时,导航环境。在本文中,我们考虑这种信息获取任务,但在对抗性环境下,攻击可能暂时使机器人传感器失效。我们提出第一个退缩地平线算法,目的是针对任何几起袭击进行强大和适应性适应性多机器人规划,我们称之为 " 弹性积极信息测距(RAIN) " 。 REAIN以在线方式呼吁一个野生轨迹规划(RTP)子路程,在外观规划范围内计划攻击-气动控制投入。我们用其不最优性来量化RTP的性能。 我们的理论分析以组合优化中引入的曲线概念为基础。我们用三种信息获取假想来评价RAIN:多目标跟踪、随机测网绘图以及持续监视。这些假想在C+和统一模型中模拟攻击性偏差性规划(甚至模拟),在高比率的模拟中,在现实和高比率上进行。