Probabilistic roadmap motion planning for deformable objects

O. Burchan Bayazit, Jyh Ming Lien, Nancy M. Amato

Research output: Contribution to journalConference articlepeer-review


In this paper, we investigate methods for motion planning for deformable robots. Our framework is based on a probabilistic roadmap planner. As with traditional motion planning, the planner's goal is to find a valid path for the robot. Unlike typical motion planning, the robot is allowed to changes its shape (deform) to avoid collisions as it moves along the path. We propose a two-stage approach. First an 'approximate' path which might contain collisions is found. Next, we attempt to correct any collisions on this path by deforming the robot. We propose and analyze two methods for performing the deformations. Both techniques are inspired by physically correct behavior, but are more efficient than completely physically correct methods. Our approach can be applied in several domains, including flexible robots, computer modeling and animation, and biological simulations.

Original languageEnglish (US)
Pages (from-to)2126-2133
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - 2002
Externally publishedYes
Event2002 IEEE International Conference on Robotics and Automation - Washington, DC, United States
Duration: May 11 2002May 15 2002

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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