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Resilient Active Target Tracking With Multiple Robots
Journal article   Open access   Peer reviewed

Resilient Active Target Tracking With Multiple Robots

Lifeng Zhou, Vasileios Tzoumas, George J. Pappas and Pratap Tokekar
IEEE robotics and automation letters, v 4(1), pp 129-136
Jan 2019
DOI: https://doi.org/10.1109/LRA.2018.2881296
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1109/LRA.2018.2881296View
Published, Version of Record (VoR) Restricted

Abstract

Approximation algorithms Multi-robot systems planning Robot kinematics Robot sensing systems robust/adaptive control of robotic systems scheduling and coordination Target tracking Trajectory
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.

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67 citations in Web of Science
68 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Robotics
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