Path planning for improved visibility using a probabilistic road map

Matthew Baumann, Simon Léonard, Elizabeth A. Croft, James J. Little

Research output: Contribution to journalArticleResearchpeer-review

41 Citations (Scopus)

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-target-tracking tasks for manipulators that use point-to-point commands with interpolated joint motion.

Original languageEnglish
Article number5361334
Pages (from-to)195-200
Number of pages6
JournalIEEE Transactions on Robotics
Volume26
Issue number1
DOIs
Publication statusPublished - 5 Feb 2010
Externally publishedYes

Keywords

  • Computer vision
  • Path planning for industrial manipulators
  • Sensor positioning
  • Visual servoing

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