The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Gross motion planning—a survey
Open accessAbstract
Motion planning is one of the most important areas of robotics research. The complexity of the motion-planning problem has hindered the development of practical algorithms. This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments. The general issues in motion planning are explained. Recent approaches and their performances are briefly described, and possible future research directions are discussed.
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Citations
Cites
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Khatib, O. (1986) · The International Journal of Robotics Research · doi:10.1177/027836498600500106 · Cited by 7,894
Cited by
A Review of Motion Planning Techniques for Automated Vehicles
Gonzalez, D., Perez, J., Milanes, V., & Nashashibi, F. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498841 · Cited by 1,670
A randomized roadmap method for path and manipulation planning
Amato, N. M., & Wu, Y. (2002) · Proceedings of IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1996.503582 · Cited by 369
Metadata
- Authors
- Hwang, Y. K., & Ahuja, N.
- Venue
- ACM Computing Surveys
- Published
- 1992-09
- Volume
- 24
- Issue
- 3
- Pages
- 219-291
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2051626883
- Cited by
- 826
- References
- 1
- FWCI
- 22.13
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Hwang, Y. K., & Ahuja, N. (1992). Gross motion planning—a survey. ACM Computing Surveys. https://doi.org/10.1145/136035.136037
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.
