Anytime computation of time-optimal off-road vehicle maneuvers using the RRT*
Author(s)Jeon, Jeong hwan; Karaman, Sertac; Frazzoli, Emilio
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Incremental sampling-based motion planning algorithms such as the Rapidly-exploring Random Trees (RRTs) have been successful in efficiently solving computationally challenging motion planning problems involving complex dynamical systems. A recently proposed algorithm, called the RRT*, also provides asymptotic optimality guarantees, i.e., almost-sure convergence to optimal trajectories (which the RRT algorithm lacked) while maintaining the computational efficiency of the RRT algorithm. In this paper, time-optimal maneuvers for a high-speed off-road vehicle taking tight turns on a loose surface are studied using the RRT* algorithm. Our simulation results show that the aggressive skidding maneuver, usually called the trail-braking maneuver, naturally emerges from the RRT* algorithm as the minimum-time trajectory. Along the way, we extend the RRT* algorithm to handle complex dynamical systems, such as those that are described by nonlinear differential equations and involve high-dimensional state spaces, which may be of independent interest. We also exploit the RRT* as an anytime computation framework for nonlinear optimization problems.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
Proceedings of the IEEE Conference on Decision and Control and European Control
Institute of Electrical and Electronics Engineers (IEEE)
Jeon, Jeong hwan, Sertac Karaman, and Emilio Frazzoli. “Anytime computation of time-optimal off-road vehicle maneuvers using the RRT*.” In IEEE Conference on Decision and Control and European Control Conference, 3276-3282. Institute of Electrical and Electronics Engineers, 2011.
Author's final manuscript