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2007
Springer

State Space Sampling of Feasible Motions for High Performance Mobile Robot Navigation in Highly Constrained Environments

9 years 6 months ago
State Space Sampling of Feasible Motions for High Performance Mobile Robot Navigation in Highly Constrained Environments
Sampling in the space of controls or actions is a well-established method for ensuring feasible local motion plans. However, as mobile robots advance in performance and competence in complex outdoor environments, this classical motion planning technique ceases to be effective. When environmental constraints severely limit the space of acceptable motions or when global motion planning expresses strong preferences, a state space sampling strategy is more effective. While this has been clear for some time, the practical question is how to achieve it while also satisfying the severe constraints of vehicle dynamic feasibility. This paper presents an effective algorithm for state space sampling based on a model-based trajectory generation approach. This method enables high-speed navigation in highly constrained and/or partially known environments such as trails, roadways, and dense off-road obstacle fields.
Thomas M. Howard, Colin J. Green, Alonzo Kelly
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where FSR
Authors Thomas M. Howard, Colin J. Green, Alonzo Kelly
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