Planning Algorithms
LQR-trees: Feedback Motion Planning via Sums-of-Squares Verification
International Journal of Robotics Research
Motion planning with dynamics by a synergistic combination of layers of planning
IEEE Transactions on Robotics
Bounding on rough terrain with the LittleDog robot
International Journal of Robotics Research
Randomized path planning on manifolds based on higher-dimensional continuation
International Journal of Robotics Research
International Journal of Robotics Research
International Journal of Robotics Research
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The reduction of the kinematics and/or dynamics of a high-DOF robotic manipulator to a low-dimension "task space" has proven to be an invaluable tool for designing feedback controllers. When obstacles or other kinodynamic constraints complicate the feedback design process, motion planning techniques can often still find feasible paths, but these techniques are typically implemented in the high-dimensional configuration (or state) space. Here we argue that providing a Voronoi bias in the task space can dramatically improve the performance of randomized motion planners, while still avoiding non-trivial constraints in the configuration (or state) space. We demonstrate the potential of task-space search by planning collision-free trajectories for a 1500 link arm through obstacles to reach a desired end-effector position.