RRTs for Nonlinear, Discrete, and Hybrid Planning and Control

Michael S. Branicky, Michael M. Curtiss, Joshua A. Levine, Stuart B. Morgan

Research output: Contribution to journalConference articlepeer-review

50 Scopus citations


In this paper, we describe a planning and control approach in terms of sampling using Rapidly-exploring Random Trees (RRTs), which were introduced by LaValle. We review RRTs for motion planning and show how to use them to solve standard nonlinear control problems. We extend them to the case of hybrid systems and describe our modifications to LaValle's Motion Strategy Library to allow for hybrid motion planning. Finally, we extend them to purely discrete spaces (using heuristic evaluation as a distance metric) and provide computational experiments comparing them to conventional methods, such as A*.

Original languageEnglish (US)
Pages (from-to)657-663
Number of pages7
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 2003
Externally publishedYes
Event42nd IEEE Conference on Decision and Control - Maui, HI, United States
Duration: Dec 9 2003Dec 12 2003

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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