Efficient sampling-based motion planning with asymptotic near-optimality guarantees for systems with dynamics

@article{Littlefield2013EfficientSM,
  title={Efficient sampling-based motion planning with asymptotic near-optimality guarantees for systems with dynamics},
  author={Zakary Littlefield and Yanbo Li and Kostas E. Bekris},
  journal={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2013},
  pages={1779-1785}
}
Recent motion planners, such as RRT*, that achieve asymptotic optimality require a local planner, which connects two states with a trajectory. For systems with dynamics, the local planner corresponds to a two-point boundary value problem (BVP) solver, which is not always available. Furthermore, asymptotically optimal solutions tend to increase computational costs relative to alternatives, such as RRT, that focus on feasibility. This paper describes a sampling-based solution with the following… CONTINUE READING
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