Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance
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 d...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1650007/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849229141051703296 |
|---|---|
| author | Jian Li Jian Li Yuan Gao Yuan Gao Zheng Li Zheng Li Weijian Zhang Weijian Zhang Weilin Yu Weilin Yu Yating Hu He Liu Changtian Li |
| author_facet | Jian Li Jian Li Yuan Gao Yuan Gao Zheng Li Zheng Li Weijian Zhang Weijian Zhang Weilin Yu Weilin Yu Yating Hu He Liu Changtian Li |
| author_sort | Jian Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0a407710db16494fa10a6db9b5907edb |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-0a407710db16494fa10a6db9b5907edb2025-08-22T05:26:59ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-08-011610.3389/fpls.2025.16500071650007Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidanceJian Li0Jian Li1Yuan Gao2Yuan Gao3Zheng Li4Zheng Li5Weijian Zhang6Weijian Zhang7Weilin Yu8Weilin Yu9Yating Hu10He Liu11Changtian Li12College of Information Technology, Jilin Agricultural University, Changchun, ChinaJilin Province Cross-Regional Collaborative Innovation Center for Agricultural Intelligent Equipment, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaJilin Province Cross-Regional Collaborative Innovation Center for Agricultural Intelligent Equipment, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaJilin Province Cross-Regional Collaborative Innovation Center for Agricultural Intelligent Equipment, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaJilin Province Cross-Regional Collaborative Innovation Center for Agricultural Intelligent Equipment, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaJilin Province Cross-Regional Collaborative Innovation Center for Agricultural Intelligent Equipment, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Engineering and Technology, Jilin Agricultural University, Changchun, Jilin, ChinaEngineering Research Center of Edibleand Medicinal Fungi, Ministry of Education, Jilin Agricultural University Changchun, Changchun, ChinaTraditional 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1650007/fullprecision agricultureA*-MSRRT* algorithmadaptive node allocationpath planningUAV |
| spellingShingle | Jian Li Jian Li Yuan Gao Yuan Gao Zheng Li Zheng Li Weijian Zhang Weijian Zhang Weilin Yu Weilin Yu Yating Hu He Liu Changtian Li Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance Frontiers in Plant Science precision agriculture A*-MSRRT* algorithm adaptive node allocation path planning UAV |
| title | Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance |
| title_full | Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance |
| title_fullStr | Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance |
| title_full_unstemmed | Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance |
| title_short | Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance |
| title_sort | multi stage bidirectional informed rrt plant protection uav path planning method based on a algorithm domain guidance |
| topic | precision agriculture A*-MSRRT* algorithm adaptive node allocation path planning UAV |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1650007/full |
| work_keys_str_mv | AT jianli multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT jianli multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT yuangao multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT yuangao multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT zhengli multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT zhengli multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT weijianzhang multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT weijianzhang multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT weilinyu multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT weilinyu multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT yatinghu multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT heliu multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance AT changtianli multistagebidirectionalinformedrrtplantprotectionuavpathplanningmethodbasedonaalgorithmdomainguidance |