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.

中文翻译：

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与机器人团队进行弹性主动信息采集

协作自主的新兴应用（例如多目标跟踪，未知地图探索和持久监视）要求机器人规划路径来导航环境，同时最大化通过车载传感器收集的信息。在本文中，我们考虑了此类信息获取任务，但在对抗性环境中，攻击可能会暂时使机器人的传感器失效。我们提出了第一个后退地平线算法，旨在针对任何数量的攻击进行健壮和自适应的多机器人规划，我们将其称为弹性主动信息获取（RAIN）。RAIN以在线方式调用“稳健轨迹规划”（RTP）子例程，该程序在超前计划范围内规划攻击稳健的控制输入。我们通过限制RTP的次优程度来量化其性能。我们基于组合优化中引入的曲率概念进行理论分析。我们在三种信息获取方案中评估RAIN：多目标跟踪，占用网格映射和持久监视。这些场景在C ++和基于Unity的模拟器中进行了模拟。在所有模拟中，RAIN都是实时运行的，即使在存在大量攻击的情况下，它也能针对最新的基准信息获取算法表现出卓越的性能。我们还展示了RAIN对各种攻击模型（最坏情况和随机攻击）以及不同的重新计划率的鲁棒性和有效性。这些场景在C ++和基于Unity的模拟器中进行了模拟。在所有模拟中，RAIN都是实时运行的，即使在存在大量攻击的情况下，它也能针对最新的基准信息获取算法表现出卓越的性能。我们还展示了RAIN对各种攻击模型（最坏情况和随机攻击）以及不同的重新计划率的鲁棒性和有效性。这些场景在C ++和基于Unity的模拟器中进行了模拟。在所有模拟中，RAIN都是实时运行的，即使在存在大量攻击的情况下，它也能针对最新的基准信息获取算法表现出卓越的性能。我们还展示了RAIN对各种攻击模型（最坏情况和随机攻击）以及不同的重新计划率的鲁棒性和有效性。