Resilient Active Target Tracking With Multiple Robots

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 m...

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Published in:	IEEE robotics and automation letters Vol. 4; no. 1; pp. 129 - 136
Main Authors:	Zhou, Lifeng, Tzoumas, Vasileios, Pappas, George J., Tokekar, Pratap
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation Approximation algorithms Computer simulation Failure Mathematical analysis Multi-robot systems Multiple robots Multiple target tracking planning Robot dynamics Robot kinematics Robot sensing systems Robots robust/adaptive control of robotic systems scheduling and coordination Sensitivity analysis Sonar Target tracking Trajectory
ISSN:	2377-3766, 2377-3766
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Abstract	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 be 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 MATLAB and Gazebo simulations and a sensitivity analysis; we focus on scenarios that involve a known number of distinguishable targets.
AbstractList	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 be 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 MATLAB and Gazebo simulations and a sensitivity analysis; we focus on scenarios that involve a known number of distinguishable targets.
Author	Zhou, Lifeng Tokekar, Pratap Tzoumas, Vasileios Pappas, George J.
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SubjectTerms	Algorithms Approximation Approximation algorithms Computer simulation Failure Mathematical analysis Multi-robot systems Multiple robots Multiple target tracking planning Robot dynamics Robot kinematics Robot sensing systems Robots robust/adaptive control of robotic systems scheduling and coordination Sensitivity analysis Sonar Target tracking Trajectory
Title	Resilient Active Target Tracking With Multiple Robots
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