A series of kinodynamic sampling-based planners have appeared over the last decade to deal with high dimensional problems for robots with realistic motion constraints. Yet, offline sampling-based planners only work in static and known environments, suffer from unbounded memory requirements and the produced paths tend to contain a lot of unnecessary maneuvers. This paper describes an online replanning algorithm which is flexible and extensible. Our results show that using a sampling-based planner in a loop, we can guide the robot to its goal using a low dimensional navigation function. We obtain higher success rates and shorter solution paths in a series of problems using only bounded memory. ©2008 IEEE.
CITATION STYLE
Tsianos, K. I., & Kavraki, L. E. (2008). Replanning: A powerful planning strategy for hard kinodynamic problems. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 1667–1672). https://doi.org/10.1109/IROS.2008.4650965
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