The minimum constraint removal problem with three robotics applications

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Hauser, K

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 33 / 1

Start / End Page

  • 5 - 17

Electronic International Standard Serial Number (EISSN)

  • 1741-3176

International Standard Serial Number (ISSN)

  • 0278-3649

Digital Object Identifier (DOI)

  • 10.1177/0278364913507795

Citation Source

  • Scopus