Authors: Mukulika Ghosh, Shawna Thomas, Marco Morales, Sam Rodriguez, Nancy M. Amato

Venue: In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)
DOI: 10.1109/IROS.2016.7759841
Link to Publication

Abstract:
Sampling-based motion planning is the state-of-the-art technique for solving challenging motion planning problems in a wide variety of domains. While generally successful, their performance suffers from increasing problem complexity. In many cases, the full problem complexity is not needed for the entire solution. We present a hierarchical aggregation framework that groups and models sets of obstacles based on the currently needed level of detail. The hierarchy enables sampling to be performed using the simplest and most conservative representation of the environment possible in that region. Our results show that this scheme improves planner performance irrespective of the underlying sampling method and input problem. In many cases, improvement is significant, with running times often less than 60% of the original planning time. 

@inproceedings{Ghosh-mpuhao-2016, 
 author = {Ghosh, Mukulika and Thomas, Shawna and Morales, Marco and Rodriguez, Sam and Amato, Nancy M}, 
 booktitle = {2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
 organization = {IEEE}, 
 pages = {5716--5721}, 
 title = {Motion planning using hierarchical aggregation of workspace obstacles}, 
 year = {2016} 
}