Long-distance target localization optimization algorithm based on single robot moving path planning
Abstract To address the problem of low positioning accuracy for long-distance static targets, we propose an optimized algorithm for long-distance target localization (LTLO) based on single-robot moving path planning. The algorithm divides the robot’s movement area into hexagonal grids and introduces...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09428-7 |
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| author | Yourong Chen Ke Wu Yidan Guo Kehua Zhao Liyuan Liu |
| author_facet | Yourong Chen Ke Wu Yidan Guo Kehua Zhao Liyuan Liu |
| author_sort | Yourong Chen |
| collection | DOAJ |
| description | Abstract To address the problem of low positioning accuracy for long-distance static targets, we propose an optimized algorithm for long-distance target localization (LTLO) based on single-robot moving path planning. The algorithm divides the robot’s movement area into hexagonal grids and introduces constraints on stopping position selection and non-redundant locations. Based on image parallelism, we propose a method for calculating the relative position of the target using sensing information from two positions. Additionally, an improved hierarchical density-Based spatial clustering of applications with noise (HDBSCAN) algorithm is developed to fuse the relative coordinates of multiple targets. Furthermore, we establish the corresponding constraints for long-distance target localization and construct a target localization optimization model based on single-robot path planning. To solve this model, we employ a double deep Q-network and propose a reward strategy based on coordinate fusion error. This approach solves the optimization model and obtains the optimal target positions and path trajectories, thereby improving the positioning accuracy for long-distance targets. Experimental results demonstrate that for static targets at distances ranging from 100 to 500 meters, LTLO outperforms traditional monocular visual localization (TMVL), monocular global geolocation (MGG) and long-range binocular vision target geolocation (LRBVTG) by obtaining an optimal path to identify target positions, maintaining a relative localization error within 4% and an absolute localization error within 6%. |
| format | Article |
| id | doaj-art-2bf02cc03dd04ee787639447610fbc06 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2bf02cc03dd04ee787639447610fbc062025-08-20T03:46:04ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-09428-7Long-distance target localization optimization algorithm based on single robot moving path planningYourong Chen0Ke Wu1Yidan Guo2Kehua Zhao3Liyuan Liu4College of Information Science and Technology, Zhejiang Shuren UniversitySchool of Information Engineering, Huzhou UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityDepartment of Decision System Sciences, Saint Joseph’s UniversityAbstract To address the problem of low positioning accuracy for long-distance static targets, we propose an optimized algorithm for long-distance target localization (LTLO) based on single-robot moving path planning. The algorithm divides the robot’s movement area into hexagonal grids and introduces constraints on stopping position selection and non-redundant locations. Based on image parallelism, we propose a method for calculating the relative position of the target using sensing information from two positions. Additionally, an improved hierarchical density-Based spatial clustering of applications with noise (HDBSCAN) algorithm is developed to fuse the relative coordinates of multiple targets. Furthermore, we establish the corresponding constraints for long-distance target localization and construct a target localization optimization model based on single-robot path planning. To solve this model, we employ a double deep Q-network and propose a reward strategy based on coordinate fusion error. This approach solves the optimization model and obtains the optimal target positions and path trajectories, thereby improving the positioning accuracy for long-distance targets. Experimental results demonstrate that for static targets at distances ranging from 100 to 500 meters, LTLO outperforms traditional monocular visual localization (TMVL), monocular global geolocation (MGG) and long-range binocular vision target geolocation (LRBVTG) by obtaining an optimal path to identify target positions, maintaining a relative localization error within 4% and an absolute localization error within 6%.https://doi.org/10.1038/s41598-025-09428-7Localization OptimizationMoving Path PlanningLong-distance TargetSingle Robot |
| spellingShingle | Yourong Chen Ke Wu Yidan Guo Kehua Zhao Liyuan Liu Long-distance target localization optimization algorithm based on single robot moving path planning Scientific Reports Localization Optimization Moving Path Planning Long-distance Target Single Robot |
| title | Long-distance target localization optimization algorithm based on single robot moving path planning |
| title_full | Long-distance target localization optimization algorithm based on single robot moving path planning |
| title_fullStr | Long-distance target localization optimization algorithm based on single robot moving path planning |
| title_full_unstemmed | Long-distance target localization optimization algorithm based on single robot moving path planning |
| title_short | Long-distance target localization optimization algorithm based on single robot moving path planning |
| title_sort | long distance target localization optimization algorithm based on single robot moving path planning |
| topic | Localization Optimization Moving Path Planning Long-distance Target Single Robot |
| url | https://doi.org/10.1038/s41598-025-09428-7 |
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