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State space sampling of feasible motions for high‐performance mobile robot navigation in complex environments
Author(s) -
Howard Thomas M.,
Green Colin J.,
Kelly Alonzo,
Ferguson Dave
Publication year - 2008
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20244
Subject(s) - mobile robot , motion planning , obstacle , obstacle avoidance , robot , computer science , sampling (signal processing) , trajectory , state space , control theory (sociology) , motion (physics) , limit (mathematics) , space (punctuation) , state (computer science) , configuration space , control engineering , artificial intelligence , simulation , engineering , computer vision , mathematics , algorithm , control (management) , geography , operating system , mathematical analysis , statistics , physics , archaeology , filter (signal processing) , astronomy , quantum mechanics
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 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. Although this has been evident for some time, the practical question is how to achieve it while also satisfying the severe constraints of vehicle dynamic feasibility. The paper presents an effective algorithm for state space sampling utilizing 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. © 2008 Wiley Periodicals, Inc.