Topology-Guided Roadmap Construction With Dynamic Region Sampling
| Autor: | Diane Uwacu, Jory Denny, Nancy M. Amato, Read Sandstrom |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Distributed computing Biomedical Engineering 010103 numerical & computational mathematics 02 engineering and technology Workspace Probabilistic roadmap 01 natural sciences 020901 industrial engineering & automation Artificial Intelligence Leverage (statistics) Motion planning 0101 mathematics Robot kinematics business.industry Mechanical Engineering Probabilistic logic Mobile robot Robotics Computer Science Applications Human-Computer Interaction Control and Systems Engineering Topological skeleton Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | IEEE Robotics and Automation Letters. 5:6161-6168 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2020.3010487 |
| Popis: | Many types of planning problems require discovery of multiple pathways through the environment, such as multi-robot coordination or protein ligand binding. The Probabilistic Roadmap (PRM) algorithm is a powerful tool for this case, but often cannot efficiently connect the roadmap in the presence of narrow passages. In this letter, we present a guidance mechanism that encourages the rapid construction of well-connected roadmaps with PRM methods. We leverage a topological skeleton of the workspace to track the algorithm's progress in both covering and connecting distinct neighborhoods, and employ this information to focus computation on the uncovered and unconnected regions. We demonstrate how this guidance improves PRM's efficiency in building a roadmap that can answer multiple queries in both robotics and protein ligand binding applications. |
| Databáze: | OpenAIRE |
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