The minimum constraint removal problem with three robotics applications
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This paper formulates a new minimum constraint removal (MCR) motion planning problem in which the objective is to remove the fewest geometric constraints necessary to connect a start and goal state with a free path. It describes a probabilistic roadmap motion planner for MCR in continuous configuration spaces that operates by constructing increasingly refined roadmaps, and efficiently solves discrete MCR problems on these networks. A number of new theoretical results are given for discrete MCR, including a proof that it is NP-hard by reduction from SET-COVER. Two search algorithms are described that perform well in practice. The motion planner is proven to produce the optimal MCR with probability approaching 1 as more time is spent, and its convergence rate is improved with various efficient sampling strategies. It is demonstrated on three example applications: generating human-interpretable excuses for failure, motion planning under uncertainty, and rearranging movable obstacles. © The Author(s) 2013.
Published Version (Please cite this version)10.1177/0278364913507795
Publication InfoHauser, Kris (2014). The minimum constraint removal problem with three robotics applications. International Journal of Robotics Research, 33(1). pp. 5-17. 10.1177/0278364913507795. Retrieved from https://hdl.handle.net/10161/10778.
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Adjunct Associate Professor in the Department of Electrical and Computer Engineering
Robot motion planning and control, semiautonomous robots, and integrating perception and planning. Applications of this research have included automated vehicle collision avoidance, robotic manipulation, robot-assisted medicine, and legged locomotion.