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
Hlavní autoři: Huang, Lu, Jing, Xingjian
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 14.10.2024
Témata:
asymptotically optimal
Costs
dynamic environments
Dynamics
Heuristic algorithms
Intelligent robots
lazy search
lifelong planning
Maintenance engineering
Motion planning
Path planning
Planning
sampling-based
Simulation
ISSN:2153-0866
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Shrnutí: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.
ISSN:2153-0866
DOI:10.1109/IROS58592.2024.10802657