The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Dynamic path planning for a mobile automaton with limited information on the environment
Abstract
The problem of path planning is studied for the case of a mobile robot moving in an environment filled with obstacles whose shape and positions are not known. Under the accepted model, the automaton knows its own and the target coordinates, and has a "sensory" feedback which provides it with local information on its immediate surroundings. Ibis information is shown to be sufficient to guarantee reaching a global objective (the target), while generating reasonable (if not optimal) paths. A lower bound on the length of paths generated by any algorithm operating with uncertainty is formulated, and two nonheuristic path planning algorithms are described. In the algorithms, motion planning is done continuously (dynamically), based on the automaton's current position and on its feedback. The effect of additional sources of information (e.g., from a vision sensor) on the outlined approach is discussed.
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Citations
Cites
Spatial Planning: A Configuration Space Approach
Lozano-Perez (1983) · IEEE Transactions on Computers · doi:10.1109/tc.1983.1676196 · Cited by 2,113
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The Stanford Cart and the CMU Rover
Moravec, H. P. (1983) · Proceedings of the IEEE · doi:10.1109/proc.1983.12684 · Cited by 309
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On the Movement of Robot Arms in 2-Dimensional Bounded Regions
Hopcroft, J. E., Joseph, D., & Whitesides, S. H. (1985) · SIAM Journal on Computing · doi:10.1137/0214025 · Cited by 67
Continuous motion planning in unknown environment for a 3D cartesian robot arm
Lumelsky, V. (1986) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1986.1087621 · Cited by 32
Self-citationOn the movement of robot arms in 2-dimensional bounded regions
Hopcroft, J. E., Joseph, D., & Whitesides, S. H. (1982) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1982.29 · Cited by 27
Cited by
Optimal and Efficient Path Planning for Partially Known Environments
Stentz, A. (1997) · Intelligent Unmanned Ground Vehicles · doi:10.1007/978-1-4615-6325-9_11 · Cited by 1,233
Optimal and efficient path planning for partially-known environments
Stentz, A. (2002) · Proceedings of the 1994 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1994.351061 · Cited by 929
Hoy, M., Matveev, A. S., & Savkin, A. V. (2015) · Robotica · doi:10.1017/s0263574714000289 · Cited by 469
Path Planning for Autonomous Mobile Robots: A Review
Sánchez-Ibáñez, J. R., Pérez-del-Pulgar, C. J., & García-Cerezo, A. (2021) · Sensors · doi:10.3390/s21237898 · Cited by 462
Geometric Shortest Paths and Network Optimization
Mitchell, J. S. B. (2000) · Handbook of Computational Geometry · doi:10.1016/b978-044482537-7/50016-4 · Cited by 427
Metadata
- Authors
- Lumelsky, V., & Stepanov, A.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1986-11
- Volume
- 31
- Issue
- 11
- Pages
- 1058-1063
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2098412395
- Cited by
- 388
- References
- 11
- FWCI
- 5.74
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
