Asymptotically Optimal Lazy Lifelong Sampling-based Algorithm for Efficient Motion Planning in Dynamic Environments
The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates f...
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| Vydáno v: | Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems s. 8861 - 8867 |
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14.10.2024
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| ISSN: | 2153-0866 |
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| Abstract | The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and thereby reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Simulation results demonstrate that the algorithm outperforms various state-of-the-art sampling-based planners in addressing both static and dynamic motion planning problems. |
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| AbstractList | The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and thereby reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Simulation results demonstrate that the algorithm outperforms various state-of-the-art sampling-based planners in addressing both static and dynamic motion planning problems. |
| Author | Huang, Lu Jing, Xingjian |
| Author_xml | – sequence: 1 givenname: Lu surname: Huang fullname: Huang, Lu email: lhuang98-c@my.cityu.edu.hk organization: City University of Hongkong,Department of Mechanical Engineering,Kowloon,Hong Kong SAR – sequence: 2 givenname: Xingjian surname: Jing fullname: Jing, Xingjian email: xingjing@cityu.edu.hk organization: City University of Hongkong,Department of Mechanical Engineering,Kowloon,Hong Kong SAR |
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| Snippet | The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and... |
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| StartPage | 8861 |
| SubjectTerms | asymptotically optimal Costs dynamic environments Dynamics Heuristic algorithms Intelligent robots lazy search lifelong planning Maintenance engineering Motion planning Path planning Planning sampling-based Simulation |
| Title | Asymptotically Optimal Lazy Lifelong Sampling-based Algorithm for Efficient Motion Planning in Dynamic Environments |
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