Motion Planning Using Hierarchical Aggregation of Workspace Obstacles**This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-1423111, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11 and by Asociacion Mexicana de Cultura A.C. | Conference Paper individual record
abstract

© 2016 IEEE. Sampling-based motion planning is the state-oftheart 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.

author list (cited authors)
Ghosh, M., Thomas, S., Morales, M., Rodriguez, S., & Amato, N. M.
publication date
2016
publisher
IEEE Publisher
citation count

0