Anytime computation of time-optimal off-road vehicle maneuvers using the RRT

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...

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Veröffentlicht in:2011 50th IEEE Conference on Decision and Control and European Control Conference S. 3276 - 3282
Hauptverfasser: Jeon, Jeong hwan, Karaman, Sertac, Frazzoli, Emilio
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2011
Schlagworte:
Heuristic algorithms
Measurement
Optimization
Planning
Trajectory
Vehicle dynamics
Vehicles
ISBN:9781612848006, 1612848001
ISSN:0191-2216
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Zusammenfassung: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.
ISBN:9781612848006
1612848001
ISSN:0191-2216
DOI:10.1109/CDC.2011.6161521