TY - GEN
T1 - C-space subdivision and integration in feature-sensitive motion planning
AU - Morales, Marco A.A.
AU - Tapia, Lydia
AU - Pearce, Roger
AU - Rodriguez, Samuel
AU - Amato, Nancy M.
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - There are many randomized motion planning techniques, but it is often difficult to determine what planning method to apply to best solve a problem. Planners have their own strengths and weaknesses, and each one is best suited to a specific type of problem. In previous work, we proposed a meta-planner that, through analysis of the problem features, subdivides the instance into regions and determines which planner to apply in each region. The results obtained with our prototype system were very promising even though it utilized simplistic strategies for all components. Even so, we did determine that strategies for problem subdivision and for combination of partial regional solutions have a crucial impact on performance. In this paper, we propose new methods for these steps to improve the performance of the meta-planner. For problem subdivision, we propose two new methods: a method based on 'gaps' and a method based on information theory. For combining partial solutions, we propose two new methods that concentrate on neighboring areas of the regional solutions. We present results that show the performance gain achieved by utilizing these new strategies.
AB - There are many randomized motion planning techniques, but it is often difficult to determine what planning method to apply to best solve a problem. Planners have their own strengths and weaknesses, and each one is best suited to a specific type of problem. In previous work, we proposed a meta-planner that, through analysis of the problem features, subdivides the instance into regions and determines which planner to apply in each region. The results obtained with our prototype system were very promising even though it utilized simplistic strategies for all components. Even so, we did determine that strategies for problem subdivision and for combination of partial regional solutions have a crucial impact on performance. In this paper, we propose new methods for these steps to improve the performance of the meta-planner. For problem subdivision, we propose two new methods: a method based on 'gaps' and a method based on information theory. For combining partial solutions, we propose two new methods that concentrate on neighboring areas of the regional solutions. We present results that show the performance gain achieved by utilizing these new strategies.
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U2 - 10.1109/ROBOT.2005.1570589
DO - 10.1109/ROBOT.2005.1570589
M3 - Conference contribution
AN - SCOPUS:33846121601
SN - 078038914X
SN - 9780780389144
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3114
EP - 3119
BT - Proceedings of the 2005 IEEE International Conference on Robotics and Automation
T2 - 2005 IEEE International Conference on Robotics and Automation
Y2 - 18 April 2005 through 22 April 2005
ER -