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.
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