Two manipulation planning algorithms
WAFR Proceedings of the workshop on Algorithmic foundations of robotics
Randomized query processing in robot path planning
Journal of Computer and System Sciences
On finding narrow passages with probabilistic roadmap planners
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
On the Probabilistic Foundations of Probabilistic Roadmap Planning
International Journal of Robotics Research
Athlete: A cargo handling and manipulation robot for the moon: Research Articles
Journal of Field Robotics - Special Issue on Space Robotics, Part III
Navigation among movable obstacles
Navigation among movable obstacles
Randomized multi-modal motion planning for a humanoid robot manipulation task
International Journal of Robotics Research
Learning spatial relationships between objects
International Journal of Robotics Research
Generation of whole-body optimal dynamic multi-contact motions
International Journal of Robotics Research
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Motion planning problems encountered in manipulation and legged locomotion have a distinctive multi-modal structure, where the space of feasible configurations consists of intersecting submanifolds, often of different dimensionalities. Such a feasible space does not possess expansiveness, a property that characterizes whether planning queries can be solved efficiently with traditional probabilistic roadmap (PRM) planners. In this paper we present a new PRM-based multi-modal planning algorithm for problems where the number of intersecting manifolds is finite. We also analyze the completeness properties of this algorithm. More specifically, we show that the algorithm converges quickly when each submanifold is individually expansive and establish a bound on the expected running time in that case. We also present an incremental variant of the algorithm that has the same convergence properties, but works better for problems with a large number of submanifolds by considering subsets of submanifolds likely to contain a solution path. These algorithms are demonstrated in geometric examples and in a legged locomotion planner.