Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning
Abstract Path planning for mobile robots in complex environments remains a critical challenge in autonomous robotics, where conventional meta-heuristic algorithms often constrain motion to sequential node-by-node progression. To overcome this limitation, this paper proposes a Direction-Assisted Enha...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00135-x |
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| _version_ | 1849235799871062016 |
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| author | Honglin Wan Qilin Wu Zhaochun Peng Zheng Lu |
| author_facet | Honglin Wan Qilin Wu Zhaochun Peng Zheng Lu |
| author_sort | Honglin Wan |
| collection | DOAJ |
| description | Abstract Path planning for mobile robots in complex environments remains a critical challenge in autonomous robotics, where conventional meta-heuristic algorithms often constrain motion to sequential node-by-node progression. To overcome this limitation, this paper proposes a Direction-Assisted Enhanced Black-winged Kite Algorithm (DAEBKA) that enables non-sequential path transitions through synergistic integration of optimization and directional heuristics. DAEBKA enhances the optimization capacity of the Black-winged Kite Algorithm (BKA) through two strategies, and then utilizes directional heuristics to expand the robot's motion primitives and allow it to move to distant points in a single step. Lastly, a novel graph-based integration strategy is proposed to address the issue of how to introduce directional heuristics into the optimization process of the enhanced BKA algorithm (EBKA). Experimental simulations across different scenarios have been tested, and these results demonstrate DAEBKA's superior performance: Compared to MAACO, MsAACO, and IHMACO, DAEBKA reduces turns by 11.1–38.5% while maintaining consistent path lengths. These results confirm that DAEBKA is an alternative solution for practical robotic path planning, ensuring motion smoothness. |
| format | Article |
| id | doaj-art-80295e0b00db45bc97081f3b88002a65 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-80295e0b00db45bc97081f3b88002a652025-08-20T04:02:41ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-07-0137612010.1007/s44443-025-00135-xDirection-assisted enhanced black-winged kite algorithm for mobile robot path planningHonglin Wan0Qilin Wu1Zhaochun Peng2Zheng Lu3School of Computer Science and Artificial Intelligence, Chaohu UniversitySchool of Computer Science and Artificial Intelligence, Chaohu UniversitySchool of Mechanical Engineering, Chaohu UniversitySchool of Computer Science and Artificial Intelligence, Chaohu UniversityAbstract Path planning for mobile robots in complex environments remains a critical challenge in autonomous robotics, where conventional meta-heuristic algorithms often constrain motion to sequential node-by-node progression. To overcome this limitation, this paper proposes a Direction-Assisted Enhanced Black-winged Kite Algorithm (DAEBKA) that enables non-sequential path transitions through synergistic integration of optimization and directional heuristics. DAEBKA enhances the optimization capacity of the Black-winged Kite Algorithm (BKA) through two strategies, and then utilizes directional heuristics to expand the robot's motion primitives and allow it to move to distant points in a single step. Lastly, a novel graph-based integration strategy is proposed to address the issue of how to introduce directional heuristics into the optimization process of the enhanced BKA algorithm (EBKA). Experimental simulations across different scenarios have been tested, and these results demonstrate DAEBKA's superior performance: Compared to MAACO, MsAACO, and IHMACO, DAEBKA reduces turns by 11.1–38.5% while maintaining consistent path lengths. These results confirm that DAEBKA is an alternative solution for practical robotic path planning, ensuring motion smoothness.https://doi.org/10.1007/s44443-025-00135-xPath planningMobile robotDirectional heuristicsGraph-based integration strategyBlack-winged kite algorithm |
| spellingShingle | Honglin Wan Qilin Wu Zhaochun Peng Zheng Lu Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning Journal of King Saud University: Computer and Information Sciences Path planning Mobile robot Directional heuristics Graph-based integration strategy Black-winged kite algorithm |
| title | Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning |
| title_full | Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning |
| title_fullStr | Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning |
| title_full_unstemmed | Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning |
| title_short | Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning |
| title_sort | direction assisted enhanced black winged kite algorithm for mobile robot path planning |
| topic | Path planning Mobile robot Directional heuristics Graph-based integration strategy Black-winged kite algorithm |
| url | https://doi.org/10.1007/s44443-025-00135-x |
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