Single-query motion planning with utility-guided random trees
Publication Date
2007
Journal or Book Title
PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10
Abstract
Randomly expanding trees are very effective in exploring high-dimensional spaces. Consequently, they are a powerful algorithmic approach to sampling-based single-query motion planning. As the dimensionality of the configuration space increases, however, the performance of tree-based planners that use uniform expansion degrades. To address this challenge, we present a utility-guided algorithm for the online adaptation of the random tree expansion strategy. This algorithm guides expansion towards regions of maximum utility based on local characteristics of state space. To guide exploration, the algorithm adjusts the parameters that control random tree expansion in response to state space information obtained during the planning process. We present experimental results to demonstrate that the resulting single-query planner is computationally more efficient and more robust than previous planners in challenging artificial and real-world environments.
DOI
https://doi.org/10.1109/ROBOT.2007.363983
Pages
3307-3312
Book Series Title
IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
Recommended Citation
Burns, B and Brock, O, "Single-query motion planning with utility-guided random trees" (2007). PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10. 395.
https://doi.org/10.1109/ROBOT.2007.363983