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An informative path planning framework for UAV-based terrain monitoring

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Title: An informative path planning framework for UAV-based terrain monitoring
Authors: Popovic, M
Vidal-Calleja, T
Hitz, G
Chung, JJ
Sa, I
Siegwart, R
Nieto, J
Item Type: Journal Article
Abstract: Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.
Issue Date: 1-Jul-2020
Date of Acceptance: 18-Jan-2020
URI: http://hdl.handle.net/10044/1/77011
DOI: 10.1007/s10514-020-09903-2
ISSN: 0929-5593
Publisher: Springer (part of Springer Nature)
Start Page: 889
End Page: 911
Journal / Book Title: Autonomous Robots
Volume: 44
Copyright Statement: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/.
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Robotics
Computer Science
Informative path planning
Aerial robotics
Environmental monitoring
Remote sensing
GAUSSIAN-PROCESSES
ALGORITHMS
OPTIMIZATION
EXPLORATION
Science & Technology
Technology
Computer Science, Artificial Intelligence
Robotics
Computer Science
Informative path planning
Aerial robotics
Environmental monitoring
Remote sensing
GAUSSIAN-PROCESSES
ALGORITHMS
OPTIMIZATION
EXPLORATION
Industrial Engineering & Automation
0801 Artificial Intelligence and Image Processing
0913 Mechanical Engineering
1702 Cognitive Sciences
Publication Status: Published
Open Access location: https://link.springer.com/article/10.1007/s10514-020-09903-2
Online Publication Date: 2020-02-04
Appears in Collections:Computing
Faculty of Engineering