Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 4, Issue 1, Pages 129-136Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2018.2881296
Keywords
Multi-robot systems; planning; scheduling and coordination; robust/adaptive control of robotic systems
Categories
Funding
- Army Research Laboratory Distributed and Collaborative Intelligent Systems and Technology Collaborative Research Alliance [W911NF-17-2-0181]
- National Science Foundation [1566247, 1637915]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1637915] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1566247] Funding Source: National Science Foundation
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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.
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