RRTs for nonlinear, discrete, and hybrid planning and control

Michael S. Branicky, Michael M. Curtiss, Joshua A. Levine, Stuart B. Morgan
42nd IEEE Conference on Decision and Control
December, 2003

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*.

[DOI/EE link]

@inproceedings{BCLM03-rrt,
  address   = {Maui, HI},
  author    = {Michael S. Branicky and Michael M. Curtiss and Joshua A. Levine and Stuart B. Morgan},
  booktitle = {42nd {IEEE} Conference on Decision and Control},
  ee        = {http://dx.doi.org/10.1109/CDC.2003.1272639},
  month     = {12},
  pages     = {657--663},
  publisher = {IEEE},
  title     = {{RRTs} for nonlinear, discrete, and hybrid planning and control},
  year      = {2003}
}