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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/15259

Title: Multi-UAV Resource Constrained Online Monitoring of Large-scale Spatio-temporal Environment with Homing Guarantee
Authors: Tiwari, Kshitij
Jeong, Sungmoon
Chong, Nak Young
Keywords: bi-objective optimization
informative path planning
Gaussian process
unmanned aerial vehicles
Issue Date: 2017-10-29
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Start page: 5893
End page: 5900
DOI: 10.1109/IECON.2017.8217022
Abstract: We propose a homing constrained bi-objective optimization variant of budget-limited informative path planning for monitoring a spatio-temporal environment. The objective function consists of weighted combination of two components: model performance which must be maximized and travel distance which must be bounded by the maximum operational range. Besides this, we have additional constraints that guarantee that the robots will return to home (base station) upon completion of their respective missions. Optimizing over this objective function is essentially NP-hard owing to the conflicting constituents. Moreover, the appropriate choice of weights and additional homing guarantees further adds to complications. We employ Gaussian Process (GP) model which is highly data driven i.e., the larger the amount of training data, the better the model performance. However, owing to limited resources, a robot can only collect a limited amount of training samples. Thus, with the introduction of our bi-objective cost function, it becomes possible to plan budget-limited (e.g., battery, flight time, travel distance etc.) informative tours using autonomous mobile robots to effectively select only the most informative (uncertain) locations from the environment. In this work, we develop an algorithm to autonomously choose the appropriate weights for the components based on available resources while ensuring homing and maintaining model quality. We perform simulations to verify the effectiveness of our proposed objective function on the publicly available Ozone Concentration dataset gathered from USA.
Rights: This is the author's version of the work. Copyright (C) 2017 IEEE. 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, 2017, 5893-5900. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/15259
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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