Resilient Active Target Tracking With Multiple Robots
Resilient Active Target Tracking With Multiple Robots
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DOI：
10.1109/lra.2018.2881296
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发表时间：
2019-01-01
影响因子：
5.2
通讯作者：
Tokekar, Pratap
中科院分区：
文献类型：
--
作者：
Zhou, Lifeng;Tzoumas, Vasileios;Tokekar, Pratap
多机器人目标跟踪问题包括主动规划机器人的运动来跟踪目标。实际部署的一个主要挑战是使机器人对故障具有弹性。特别是，机器人可能会在对抗性场景中受到攻击，或者它们的传感器可能会出现故障或被遮挡。在这封信中，我们将介绍规划算法的多目标跟踪，是弹性的，这样的失败。通常，弹性目标跟踪在计算上是困难的。与不存在故障的情况相反，当目标不可区分或数量未知或具有未知运动模型时，没有已知用于弹性目标跟踪的可缩放近似算法。在这封信中，我们提供了第一个这样的算法，它也有以下属性：首先，它实现了最大的弹性，因为该算法是有效的任何数量的失败。第二，它是可扩展的，因为我们的算法终止与国家的最先进的算法（非弹性）目标跟踪相同的运行时间。第三，它提供了可证明的近似界的跟踪性能，因为我们的算法保证了一个解决方案，保证他接近最优。我们量化我们的算法的近似性能使用一种新的概念的曲率单调集函数拟阵约束。最后，我们证明了我们的算法的有效性，通过MAMAS和露台模拟和敏感性分析，我们专注于涉及已知数量的可区分目标的情况。
The problem of target tracking with multiple robots consists of actively planning the motion of the robots to track the targets. A major challenge for practical deployments is to make the robots resilient to failures. In particular, robots may be attacked in adversarial scenarios, or their sensors may fail or get occluded. In this letter, we introduce planning algorithms for multi-target tracking that are resilient to such failures. In general, resilient target tracking is computationally hard. Contrary to the case where there are no failures, no scalable approximation algorithms are known for resilient target tracking when the targets are indistinguishable, or unknown in number, or with unknown motion model. In this letter, we provide the first such algorithm, which also has the following properties: First, it achieves maximal resiliency, since the algorithm is valid for any number of failures. Second, it is scalable, as our algorithm terminates with the same running time as state-of-the-art algorithms for (non-resilient) target tracking. Third, it provides provable approximation bounds on the tracking performance, since our algorithm guarantees a solution that is guaranteed to he close to the optimal. We quantify our algorithm's approximation performance using a novel notion of curvature for monotone set functions subject to matroid constraints. Finally, we demonstrate the efficacy of our algorithm through MAMAS and Gazebo simulations and a sensitivity analysis; we focus on scenarios that involve a known number of distinguishable targets.