Sampling-based reachability algorithms for control and verification of complex systems
Michael S. Branicky, Michael M. Curtiss, Joshua A. Levine, Stuart B. Morgan
13th Yale Workshop on Adaptive and Learning Systems
In this paper, we survey sampling-based reachability algorithms that may be used for planning, control, and verification of complex systems. These were inspired by two robotics motion planning algorithms: Rapidly-exploring Random Trees (RRTs) and Probabilistic RoadMaps (PRMs). Herein, we review RRTs and PRMs. We review our adaptations and extensions to nonlinear control problems, hybrid systems, and completely discrete problems. We also summarize our work on the properties of sampling-based techniques.