The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Sampling-Based Path Planning on Configuration-Space Costmaps
Open accessAbstract
This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.
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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 2×
“Note also that the probabilistic conformational roadmap [18] developed to explore molecularenergy landscapes in computational biology applications integrates a similar transition test in the Probabilistic RoadMap (PRM) framework [19].”
BackgroundMethod
Metadata
- Authors
- Jaillet, L., Cortés, J., & Siméon, T.
- Venue
- IEEE Transactions on Robotics
- Published
- 2010-08
- Volume
- 26
- Issue
- 4
- Pages
- 635-646
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2034801091
- Cited by
- 367
- References
- 1
- FWCI
- 22.85
- Type
- journal article
- Language
- en
- Open access
- green
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- Jaillet, L., Cortés, J., & Siméon, T. (2010). Sampling-Based Path Planning on Configuration-Space Costmaps. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2010.2049527
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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.
