Sampling-Based Planning and Control

In this paper, we describe a planning and control approach in terms of sampling using Rapidlyexploring Random Trees (RRTs), which were introduced in [7, 9]. 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*.

[1]  Mark W. Spong,et al.  Swing up control of the Acrobot , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[2]  Michael S. Branicky,et al.  Studies in hybrid systems: modeling, analysis, and control , 1996 .

[3]  V. Borkar,et al.  A unified framework for hybrid control: model and optimal control theory , 1998, IEEE Trans. Autom. Control..

[4]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[5]  Emilio Frazzoli,et al.  A hybrid control architecture for aggressive maneuvering of autonomous helicopters , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[6]  Steven M. LaValle,et al.  Randomized Kinodynamic Planning , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[7]  Steven M. LaValle,et al.  Quasi-randomized path planning , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[8]  Michael S. Branicky,et al.  Nonlinear and Hybrid Control Via RRTs , 2002 .

[9]  Steven M. LaValle,et al.  On the Relationship between Classical Grid Search and Probabilistic Roadmaps , 2004, Int. J. Robotics Res..