TY - GEN
T1 - Biasing samplers to improve motion planning performance
AU - Thomas, Shawna
AU - Morales, Marco
AU - Tang, Xinyu
AU - Amato, Nancy M.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=36348993702&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36348993702&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2007.363556
DO - 10.1109/ROBOT.2007.363556
M3 - Conference contribution
AN - SCOPUS:36348993702
SN - 1424406021
SN - 9781424406029
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1625
EP - 1630
BT - 2007 IEEE International Conference on Robotics and Automation, ICRA'07
T2 - 2007 IEEE International Conference on Robotics and Automation, ICRA'07
Y2 - 10 April 2007 through 14 April 2007
ER -