Sampling-based path planning for geometrically-constrained objects

Authors:
A. Rodríguez;A. Pérez;J. Rosell;L. Basañez
Affiliations:
Institute of Industrial and Control Engineering, Technical University of Catalonia, Barcelona, Spain;Institute of Industrial and Control Engineering, Technical University of Catalonia, Barcelona, Spain and Escuela Colombiana de Ingeniería "Julio Garavito", Bogotá D.C., Colombia;Institute of Industrial and Control Engineering, Technical University of Catalonia, Barcelona, Spain;Institute of Industrial and Control Engineering, Technical University of Catalonia, Barcelona, Spain
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 4
Cited 1

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Abstract

One of the key factors that affect the success and efficiency of sampling-based path planners is the obtention of samples in the more relevant regions of the workspace. This is known as importance sampling, and different approaches have already been proposed in this direction. This paper proposes a novel method to bias sampling by means of geometric constraints that reduces the sampling space to sets of lower dimensional submanifolds. These constraints may be imposed by the kinematic structure of the actuation system, by the task specification, or provided by a human user as an intuitive way to include problem knowledge to the planner. The method has been implemented and tested on a probabilistic roadmap planner giving promising results. A variant using a deterministic sampling source is also reported.