The DOI matches its Crossref record.
Motion planning in a plane using generalized Voronoi diagrams
Abstract
An algorithm for planning a collision-free path for a rectangle in a planar workspace populated with polygonal obstacles is presented. Heuristic techniques are used to plan the motion along a nominal path obtained from a generalized Voronoi diagram (GVD). The algorithm was demonstrated to be quite fast with execution times comparable to, or exceeding, those of the freeway method. Unlike the freeway method, the GVD technique can be successfully applied to difficult problems which arise in cluttered workspaces. The planned paths stay well away from the obstacles when possible and are somewhat shorter than the freeway paths due to parabolic arcs around corners. Furthermore, motion along the paths is smooth in the sense that rotations are performed during translation, not just at isolated points in the workspace.>
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Cited by
Embedded nonlinear model predictive control for obstacle avoidance using PANOC
Sathya, A., Sopasakis, P., Van Parys, R., Themelis, A., Pipeleers, G., & Patrinos, P. (2018) · 2018 European Control Conference (ECC) · doi:10.23919/ecc.2018.8550253 · Cited by 105
Metadata
- Authors
- Takahashi, O., & Schilling, R. J.
- Venue
- IEEE Transactions on Robotics and Automation
- Published
- 1989-04
- Volume
- 5
- Issue
- 2
- Pages
- 143-150
- ISSN
- 1042-296X (ISSN-L)
- OpenAlex
- W1974909294
- Cited by
- 417
- References
- 0
- FWCI
- 6.00
- Type
- journal article
- Language
- en
- Open access
- closed
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Motion planning in a plane using generalized Voronoi diagrams. https://app.cochat.ai/papers/10.1109/70.88035
- The paper
- Takahashi, O., & Schilling, R. J. (1989). Motion planning in a plane using generalized Voronoi diagrams. IEEE Transactions on Robotics and Automation. https://doi.org/10.1109/70.88035
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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.
