Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Planning Algorithms
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Manipulation planning with workspace goal regions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Motion planning with dynamics by a synergistic combination of layers of planning
IEEE Transactions on Robotics
Improving the Performance of Sampling-Based Motion Planning With Symmetry-Based Gap Reduction
IEEE Transactions on Robotics
Delaunay refinement algorithms for triangular mesh generation
Computational Geometry: Theory and Applications
Research paper: Sampling-based robot motion planning: Towards realistic applications
Computer Science Review
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This paper shows how to effectively compute collision-free and dynamically-feasible robot motion trajectories from an initial state to a goal region by combining sampling-based motion planning over the continuous state space with forward and backward discrete search over a workspace decomposition. Backward discrete search is used to estimate the cost of reaching the goal from each workspace region. Forward discrete search provides discrete plans, i.e., sequences of neighboring regions to reach the goal starting from low-cost regions. Sampling-based motion planning uses the discrete plans as a guide to expand a tree of collision-free and dynamically-feasible motion trajectories toward the goal. The proposed approach, as shown by the experiments, offers significant computational speedups over related work in solving high-dimensional motion-planning problems with dynamics.