Path Database Guidance for Motion Planning

• URL: http://arxiv.org/abs/2504.05550v1
• Date: Mon, 07 Apr 2025 23:00:31 GMT
• Title: Path Database Guidance for Motion Planning
• Authors: Amnon Attali, Praval Telagi, Marco Morales, Nancy M. Amato,
• Abstract summary: We present a new method, Path Database Guidance (PDG), which innovates on existing work in two ways.<n>First, we use the database to compute a experimentally for determining which nodes of a search tree to expand.<n>Second, in contrast to other methods that treat the database as a single fixed prior, our database updates as we search the implicitly defined robot configuration space.
• Score: 1.4078050092809555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One approach to using prior experience in robot motion planning is to store solutions to previously seen problems in a database of paths. Methods that use such databases are characterized by how they query for a path and how they use queries given a new problem. In this work we present a new method, Path Database Guidance (PDG), which innovates on existing work in two ways. First, we use the database to compute a heuristic for determining which nodes of a search tree to expand, in contrast to prior work which generally pastes the (possibly transformed) queried path or uses it to bias a sampling distribution. We demonstrate that this makes our method more easily composable with other search methods by dynamically interleaving exploration according to a baseline algorithm with exploitation of the database guidance. Second, in contrast to other methods that treat the database as a single fixed prior, our database (and thus our queried heuristic) updates as we search the implicitly defined robot configuration space. We experimentally demonstrate the effectiveness of PDG in a variety of explicitly defined environment distributions in simulation.

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