ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1650007
This article is part of the Research TopicAdvancing Plant Science with UAVs: Precision in Agricultural Sensing, Targeted Protection, and PhenotypingView all articles
Multi-stage bidirectional Informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance
Provisionally accepted
- Jilin Agriculture University, Changchun, China
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Traditional path planning algorithms often face problems such as local optimum traps and low monitoring efficiency in agricultural UAV operations, making it difficult to meet the operational requirements of complex environments in modern precision agriculture. Therefore, there is an urgent need to develop an intelligent path planning algorithm. To address this issue, this study proposes an improved Informed-RRT* path planning algorithm guided by domain-partitioned A* algorithm. The proposed algorithm employs a multi-level decomposition strategy to intelligently divide complex paths into a sequence of key sub-segments, and uses an adaptive node density allocation mechanism to dynamically respond to changes in path complexity. Finally, a dual-layer optimization framework is constructed by combining elliptical heuristic sampling with dynamic weight adjustment. Complex maps are constructed in simulation to evaluate the algorithm's performance under varying obstacle densities. Experimental results show that, compared to traditional RRT* and its improved variants, the proposed algorithm reduces computation time by 56.3%-92.5% and shortens path length by 0.42%-8.5%, while also demonstrating superior path smoothness and feasibility, as well as a more balanced distribution of search nodes. Comprehensive analysis indicates that the A*-MSRRT* (A*-Guided Multi-stage Bidirectional Informed-RRT*) algorithm has strong potential for application in complex agricultural environments.
Keywords: precision agriculture, A*-MSRRT* Algorithm, Adaptive Node Allocation, path planning, UAV
Received: 19 Jun 2025; Accepted: 21 Jul 2025.
Copyright: © 2025 Li, Gao, Li, Zhang, Yu, Hu, Liu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
He Liu, Jilin Agriculture University, Changchun, China
Chang Tian Li, Jilin Agriculture University, Changchun, China
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