Database filtering strategies for experience-based motion planning
Telagi, Praval
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https://hdl.handle.net/2142/129200
Description
Title
Database filtering strategies for experience-based motion planning
Author(s)
Telagi, Praval
Issue Date
2025-04-15
Director of Research (if dissertation) or Advisor (if thesis)
Amato, Nancy M
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Motion Planning
Machine Learning
Language
eng
Abstract
Learning from previous experience in motion planning has been shown to significantly reduce search time in new, unseen motion planning problems. Often, these experiences are stored in a database as paths and a new problem will query the database for the most relevant solutions. Prior work in experience based motion planning tends to use limited information, such as only task information, about the motion planning problem when querying a database for solutions. Additionally, many prior methods fail to provide a principled way of generating a good experience database that allows their algorithm to perform optimally. We study the effects of a variety of filtering methods on a database for a novel motion planning algorithm Path Database Guidance (PDG) to understand which method consistently minimizes the average number of collision checks performed during search. Specifically, we analyze augmenting the database by performing random filtering, cluster-based filtering, and model-based filtering. Our results illustrate the significant impact that databases can have on the performance of PDG in solving various motion planning problems. Lastly, we show our model-based filtering method consistently minimizes the average number of collision checks across problem domains using information about the environment.
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