The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Random Sampling Scheme for Path Planning
Abstract
Several randomized path planners have been proposed dur ing the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empir ically observed success. In this article, we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot 's configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilis tically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characteriz ing the "goodness" of the robot's free space, we show that the
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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 · cited 3×
“Another planner, presented in [18, 20, 21, 24], precomputes a “roadmap” (network) of simple paths connecting randomly selected configurations and tries to construct a path between any two input configurations by connecting them to this roadmap; we will refer”
BackgroundResultSelf-citation
Cited by
RRT-connect: An efficient approach to single-query path planning
Kuffner, J. J., & LaValle, S. M. (2000) · Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065) · doi:10.1109/robot.2000.844730 · Cited by 3,555
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
Guidelines in nonholonomic motion planning for mobile robots
Laumond, J. P., Sekhavat, S., & Lamiraux, F. (2006) · Lecture Notes in Control and Information Sciences · doi:10.1007/bfb0036070 · Cited by 330
Metadata
- Authors
- Barraquand, J., Kavraki, L., Latombe, J.-C., Motwani, R., Li, T.-Y., & Raghavan, P.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 1997-12
- Volume
- 16
- Issue
- 6
- Pages
- 759-774
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2134741043
- Cited by
- 277
- References
- 1
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Barraquand, J., Kavraki, L., Latombe, J.-C., Motwani, R., Li, T.-Y., & Raghavan, P. (1997). A Random Sampling Scheme for Path Planning. The International Journal of Robotics Research. https://doi.org/10.1177/027836499701600604
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
