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Informative Path Planning for Mobile Sensing with...
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Informative Path Planning for Mobile Sensing with Reinforcement Learning

Abstract

Large-scale spatial data such as air quality, thermal conditions and location signatures play a vital role in a variety of applications. Collecting such data manually can be tedious and labour intensive. With the advancement of robotic technologies, it is feasible to automate such tasks using mobile robots with sensing and navigation capabilities. However, due to limited battery lifetime and scarcity of charging stations, it is important to plan paths for the robots that maximize the utility of data collection, also known as the informative path planning (IPP) problem. In this paper, we propose a novel IPP algorithm using reinforcement learning (RL). A constrained exploration and exploitation strategy is designed to address the unique challenges of IPP, and is shown to have fast convergence and better optimality than a classical reinforcement learning approach. Extensive experiments using real-world measurement data demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in most test cases. Interestingly, unlike existing solutions that have to be re-executed when any input parameter changes, our RL-based solution allows a degree of transferability across different problem instances.

Authors

Wei Y; Zheng R

Volume

00

Pagination

pp. 864-873

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication Date

July 9, 2020

DOI

10.1109/infocom41043.2020.9155528

Name of conference

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
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