Motion planning using hierarchical aggregation of workspace obstacles
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.
Ghosh, Mukulika, Shawna Thomas, Marco Morales, Sam Rodriguez, and Nancy M. Amato. "Motion planning using hierarchical aggregation of workspace obstacles." In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5716-5721. IEEE, 2016.
IEEE International Conference on Intelligent Robots and Systems