The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Real-time obstacle avoidance for fast mobile robots
Abstract
A real-time obstacle avoidance approach for mobile robots has been developed and implemented. It permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advance toward the target. The novelty of this approach, entitled the virtual force field method, lies in the integration of two known concepts: certainty grids for obstacle representation and potential fields for navigation. This combination is especially suitable for the accommodation of inaccurate sensor data as well as for sensor fusion and makes possible continuous motion of the robot with stopping in front of obstacles. This navigation algorithm also takes into account the dynamic behavior of a fast mobile robot and solves the local minimum trap problem. Experimental results from a mobile robot running at a maximum speed of 0.78 m/s demonstrate the power of the algorithm.>
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Citations
Cites
A robust layered control system for a mobile robot
Brooks, R. (1986) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1986.1087032 · Cited by 7,799 · cited 2×
“Brooks"s implementation treats each ultrasonic range reading as a repulsive force vector.”
High resolution maps from wide angle sonar
Moravec, H., & Elfes, A. (2005) · Proceedings. 1985 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1985.1087316 · Cited by 1,772
Real-time obstacle avoidance for manipulators and mobile robots
Khatib, O. (2005) · Proceedings. 1985 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1985.1087247 · Cited by 1,757
IEEE journal of robotics and automation
Robotics, I. (1985) · Institute of Electrical and Electronics Engineers eBooks · Cited by 492
Navigation for an intelligent mobile robot
Crowley, J. (1985) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1985.1087002 · Cited by 489
Integrated path planning and dynamic steering control for autonomous vehicles
Krogh, B., & Thorpe, C. (1986) · Proceedings. 1986 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1986.1087444 · Cited by 259
Path Relaxation: Path Planning for a Mobile Robot
Thorpe, C., & Matthies, L. (1984) · doi:10.1109/oceans.1984.1152243 · Cited by 186
A sonar-based mapping and navigation system
Elfes, A. (1986) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1986.1087534 · Cited by 148
Asynchronous Distributed Control System For A Mobile Robot
Brooks, R. A., & Connell, J. H. (1987) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.937785 · Cited by 134
Dynamic world modeling for an intelligent mobile robot using a rotating ultra-sonic ranging device
Crowley, J. L. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1985.1087380 · Cited by 86
Cited by
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989381 · Cited by 1,533
Dynamic Motion Planning for Mobile Robots Using Potential Field Method
Ge, S. S., & Cui, Y. J. (2002) · Autonomous Robots · doi:10.1023/a:1020564024509 · Cited by 803
Ving: Learning open-world navigation with visual goals
Shah, D., Eysenbach, B., Kahn, G., Rhinehart, N., & Levine, S. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561936 · Cited by 81
Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors
Garzón Ramos, D., & Birattari, M. (2020) · Applied Sciences · doi:10.3390/app10134654 · Cited by 26
Training toddlers seated on mobile robots to drive indoors amidst obstacles
Chen, X., Ragonesi, C., Agrawal, S. K., & Galloway, J. C. (2010) · 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics · doi:10.1109/biorob.2010.5627809 · Cited by 8
Metadata
- Authors
- Borenstein, J., & Koren, Y.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1989-09
- Volume
- 19
- Issue
- 5
- Pages
- 1179-1187
- ISSN
- 0018-9472 (ISSN-L)
- OpenAlex
- W2069830673
- Cited by
- 1,258
- References
- 12
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- Borenstein, J., & Koren, Y. (1989). Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/21.44033
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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.
