The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
The bridge test for sampling narrow passages with probabilistic roadmap planners
Abstract
Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but narrow passages in a robot's configuration space create significant difficulty for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which boosts the sampling density inside narrow passages. The bridge test relies on simple tests of local geometry and can be implemented efficiently in high-dimensional configuration spaces. The strengths of the bridge test and uniform sampling complement each other naturally and are combined to generate the final hybrid sampling strategy. Our planner was tested on point robots and articulated robots in planar workspaces. Preliminary experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.
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
Randomized Kinodynamic Planning
LaValle, S. M., & Kuffner, J. J. (2001) · The International Journal of Robotics Research · doi:10.1177/02783640122067453 · Cited by 3,254
OBBTree: a hierarchical structure for rapid interference detection
Gottschalk, S., Lin, M. C., & Manocha, D. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237244 · Cited by 2,081
Kalos, M. H., & Whitlock, P. A. (1986) · Wiley eBooks · doi:10.1002/9783527617395 · Cited by 1,546
Bohlin, R., & Kavraki, L. E. (2000) · Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065) · doi:10.1109/robot.2000.844107 · Cited by 856
Schwartz, J. T., & Sharir, M. (1983) · Advances in Applied Mathematics · doi:10.1016/0196-8858(83)90014-3 · Cited by 801
Complexity of the mover's problem and generalizations
Reif, J. H. (1979) · 20th Annual Symposium on Foundations of Computer Science (sfcs 1979) · doi:10.1109/sfcs.1979.10 · Cited by 765
Self-citationOptimally combining sampling techniques for Monte Carlo rendering
Veach, E., & Guibas, L. J. (1995) · Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH '95 · doi:10.1145/218380.218498 · Cited by 616
Path planning in expansive configuration spaces
Hsu, D., Latombe, J.-C., & Motwani, R. (2002) · Proceedings of International Conference on Robotics and Automation · doi:10.1109/robot.1997.619371 · Cited by 552
Self-citationThe Gaussian sampling strategy for probabilistic roadmap planners
Boor, V., Overmars, M. H., & van der Stappen, A. F. (2003) · Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C) · doi:10.1109/robot.1999.772447 · Cited by 541
Metadata
- Authors
- Hsu, D., Jiang, T., Reif, J., & Sun, Z.
- Venue
- 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)
- Publisher
- IEEE
- Published
- 2004-03-30
- Volume
- 3
- Pages
- 4420-4426
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2109212155
- Cited by
- 355
- References
- 25
- FWCI
- 16.78
- Type
- conference paper
- 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: The bridge test for sampling narrow passages with probabilistic roadmap planners. https://app.cochat.ai/papers/10.1109/robot.2003.1242285
- The paper
- Hsu, D., Jiang, T., Reif, J., & Sun, Z. (2004). The bridge test for sampling narrow passages with probabilistic roadmap planners. 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422). https://doi.org/10.1109/robot.2003.1242285
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.
