An Adaptive Stepsize RRT Planning Algorithm for Open-Chain Robots

Byungchul An, Jinkyu Kim, Frank C. Park

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Motion planning algorithms that rely upon the randomly exploring random tree (RRT) typically require the user to choose an appropriate stepsize; this is generally a highly problem-dependent and time-consuming process requiring trial and error. We propose an adaptive stepsize RRT path planning algorithm for open-chain robots in which only a minimum obstacle size parameter is required as input. Exploiting the structure of an open chain's forward kinematics as well as a standard inequality bound on the operator-induced matrix norm, we derive a maximum Cartesian displacement bound between two configurations of the same robot, and use this bound to determine a maximum allowable stepsize at each iteration. Numerical experiments involving a ten-DOF planar open chain and a seven-axis industrial robot arm demonstrate the practical advantages of our algorithm over standard fixed-stepsize RRT planning algorithms.

Original languageEnglish
Article number8017418
Pages (from-to)312-319
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number1
Publication statusPublished - 2018 Jan
Externally publishedYes


  • Adaptive stepsize
  • operator norm
  • path planning
  • rapidly-exploring random tree

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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