Analysis of solutions to the time-optimal planning and execution problem
Intelligent Service Robotics
Rapid control selection through hill-climbing methods
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
Terrain traversability analysis methods for unmanned ground vehicles: A survey
Engineering Applications of Artificial Intelligence
Spline-Based RRT Path Planner for Non-Holonomic Robots
Journal of Intelligent and Robotic Systems
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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.