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Informative path planning for unmanned aerial vehicles using cost-benefit spanning tree

Published online by Cambridge University Press:  03 December 2025

Wei-Hsiang Chiu  and
Kuo-Shih Tseng Open the ORCID record for Kuo-Shih Tseng [Opens in a new window]
Wei-Hsiang Chiu
Affiliation:
Department of Mathematics, National Central University, Taoyuan City, Taiwan
Kuo-Shih Tseng*
Affiliation:
Department of Mathematics, National Central University, Taoyuan City, Taiwan
*
Corresponding author: Kuo-Shih Tseng; Email: kuoshih@math.ncu.edu.tw
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Abstract

Informative path planning (IPP) is one of key applications for unmanned aerial vehicles. It can be applied to terrain monitoring problems, which are to find the static targets from bird’s eye view. In this research, the proposed algorithm generates the cost-benefit spanning tree (CBST) to boost the IPP performance. The CBST is able to generate different tree structures based on different parameters. The proofs show that the theoretical guarantees depend on the tree structures (e.g., minimal spanning tree and shortest path tree). The simulations and experiments demonstrate that the proposed method outperforms the benchmark approaches.

Keywords

aerial roboticspath planningmotion planningnavigationcontrol of robotic systems

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 12 , December 2025 , pp. 4402 - 4428
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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