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Time - Thursday, September 29, 2016, 2:00 pm
Location - 302 HRBB

Motion Planning using Hierarchical Aggregation of Workspace Obstacles

Mukulika Ghosh
Parasol Laboratory, Department of Computer Science
Texas A&M University


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.


Mukulika Ghosh is a PhD student working under Dr. Nancy M. Amato in Parasol Lab, Texas A&M University. She completed her Masters from the Department of Computer Science and Engineering, Texas A&M University in August 2012. Her research interests include computational geometry, in particular shape approximation methods.

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