Title :
Biasing Samplers to Improve Motion Planning Performance
Author :
Thomas, Shawna ; Morales, Marco ; Xinyu Tang ; Amato, Nancy M.
Abstract :
With the success of randomized sampling-based motion planners such as probabilistic roadmap methods, much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that outperforms all other techniques for all motion planning problems. Instead, each proposed technique has different strengths and weaknesses. However, little work has been done to combine these techniques to create new distributions. In this paper, we propose to bias one sampling distribution with another such that the resulting distribution out-performs either of its parent distributions. We present a general framework for biasing samplers that is easily extendable to new distributions and can handle an arbitrary number of parent distributions by chaining them together. Our experimental results show that by combining distributions, we can out-perform existing planners. Our results also indicate that not one single distribution combination performs the best in all problems, and we identify which perform better for the specific application domains studied
Keywords :
path planning; sampling methods; statistical distributions; motion planning; probabilistic roadmap method; randomized sampling; sampler biasing; sampling distribution; Animation; Application software; Biology computing; Drugs; Filters; Motion planning; Robotics and automation; Robots; Sampling methods; US Department of Energy;
Conference_Titel :
Robotics and Automation, 2007 IEEE International Conference on
Conference_Location :
Roma
Print_ISBN :
1-4244-0601-3
Electronic_ISBN :
1050-4729
DOI :
10.1109/ROBOT.2007.363556