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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Vydáno v:IEEE robotics and automation letters Ročník 4; číslo 1; s. 129 - 136
Hlavní autoři: Zhou, Lifeng, Tzoumas, Vasileios, Pappas, George J., Tokekar, Pratap
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2018.2881296