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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
- 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.
- Graduation Semester
- 2025-05
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/129200
- Copyright and License Information
- Copyright 2025 Praval Telagi
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