The Gaussian sampling strategy for probabilistic roadmap planners

作者：V. Boor, M.H. Overmars, A. Frank van der Stappen · 年份：2003 · DOI：10.1109/robot.1999.772447 · 被引用次数：542 · 研究领域：Robotic Path Planning Algorithms、Robotics and Sensor-Based Localization、AI-based Problem Solving and Planning

Probabilistic roadmap planners (PRMs) form a relatively new technique for motion planning that has shown great potential. A critical aspect of PRM is the probabilistic strategy used to sample the free configuration space. In this paper we present a new, simple sampling strategy, which we call the Gaussian sampler, that gives a much better coverage of the difficult parts of the free configuration space. The approach uses only elementary operations which makes it suitable for many different planning problems. Experiments indicate that the technique is very efficient indeed.