The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Visibility-based probabilistic roadmaps for motion planning
Abstract
This paper presents a variant of probabilistic roadmap methods (PRM) that recently appeared as a promising approach to motion planning. We exploit a free-space structuring of the configuration space into visibility domains in order to produce small roadmaps, called visibility roadmaps. Our algorithm integrates an original termination condition related to the volume of the free space covered by the roadmap. The planner has been implemented within a software platform allowing us to address a large class of mechanical systems. Experiments show the efficiency of the approach, in particular for capturing narrow passages of collision-free configuration spaces.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 1×
“see [ 5 ]), creates nodes with all cone gurations picked uniformly at random in CSfree, checks the connections with all the nodes of the roadmap and stops after a given number of nodes is reached.”
Background
Metadata
- Authors
- Siméon, T., Laumond, J.-P., & Nissoux, C.
- Venue
- Advanced Robotics
- Publisher
- Informa UK Limited
- Published
- 2000-01
- Volume
- 14
- Issue
- 6
- Pages
- 477-493
- ISSN
- 0169-1864 (ISSN-L)
- 1568-5535
- OpenAlex
- W2161473822
- Cited by
- 329
- References
- 1
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Visibility-based probabilistic roadmaps for motion planning. https://app.cochat.ai/papers/10.1163/156855300741960
- The paper
- Siméon, T., Laumond, J.-P., & Nissoux, C. (2000). Visibility-based probabilistic roadmaps for motion planning. Advanced Robotics. https://doi.org/10.1163/156855300741960
Data sources
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.
