The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Path planning using lazy PRM
Abstract
Describes an approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner. Our algorithm builds a roadmap in the configuration space, whose nodes are the user-defined initial and goal configurations and a number of randomly generated nodes. Neighboring nodes are connected by edges representing paths between the nodes. In contrast with PRMs, our planner initially assumes that all nodes and edges in the roadmap are collision-free, and searches the roadmap at hand for a shortest path between the initial and the goal node. The nodes and edges along the path are then checked for collision. If a collision with the obstacles occurs, the corresponding nodes and edges are removed from the roadmap. Our planner either finds a new shortest path, or first updates the roadmap with new nodes and edges, and then searches for a shortest path. The above process is repeated until a collision-free path is returned. Lazy PRM is tailored to efficiently answer single planning queries, but can also be used for multiple queries. Experimental results presented in the paper show that our lazy method is very efficient in practice.
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Citations
Cites
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343
Self-citation
Cited by
The Open Motion Planning Library
Sucan, I. A., Moll, M., & Kavraki, L. E. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2205651 · Cited by 1,616
Self-citationThe bridge test for sampling narrow passages with probabilistic roadmap planners
Hsu, D., Jiang, T., Reif, J., & Sun, Z. (2004) · 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) · doi:10.1109/robot.2003.1242285 · Cited by 355
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
Xia, F., Li, C., Martín-Martín, R., Litany, O., Toshev, A., & Savarese, S. (2020) · arXiv · doi:10.48550/arxiv.2008.07792 · Cited by 33
Motions in Microseconds via Vectorized Sampling-Based Planning
Thomason, W., Kingston, Z., & Kavraki, L. E. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611190 · Cited by 28
Self-citationQuasi-static path optimization for industrial robots with dress packs
Hermansson, T., Carlson, J. S., Linn, J., & Kressin, J. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102055 · Cited by 20
Metadata
- Authors
- Bohlin, R., & Kavraki, L. E.
- Venue
- Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
- Publisher
- IEEE
- Published
- 2000
- Pages
- 521-528 vol.1
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2159722616
- Cited by
- 856
- References
- 1
- FWCI
- 19.83
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bohlin, R., & Kavraki, L. E. (2000). Path planning using lazy PRM. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065). https://doi.org/10.1109/robot.2000.844107
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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