Path planning for improved visibility using a probabilistic road map

Authors:
Matthew Baumann;Simon Léonard;Elizabeth A. Croft;James J. Little
Affiliations:
Department of Computer Science, The University of British Columbia, Vancouver, BC, Canada;Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, Canada;Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, Canada;Department of Computer Science, The University of British Columbia, Vancouver, BC, Canada
Venue:
IEEE Transactions on Robotics
Year:
2010

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Abstract

This paper focuses on the challenges of vision-based motion planning for industrial manipulators. Our approach is aimed at planning paths that are within the sensing and actuation limits of industrial hardware and software. Building on recent advances in path planning, our planner augments probabilistic road maps with vision-based constraints. The resulting planner finds collision-free paths that simultaneously avoid occlusions of an image target and keep the target within the field of view of the camera. The planner can be applied to eye-in-hand visual-targettracking tasks for manipulators that use point-to-point commands with interpolated joint motion.