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Cited by 370

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Global path planning using artificial potential fields

Warren, C.W.·2003·Proceedings, 1989 International Conference on Robotics and Automation

Abstract

The author describes a path planning technique for robotic manipulators and mobile robots in the presence of stationary obstacles. The planning consists of applying potential fields around configuration-space obstacles and using these fields to select a safe path for the robot to follow. The advantage of using potential fields in path planning is that they offer a relatively fast and effective way to solve for safe paths around obstacles. In the proposed method of path planning, a trial path is chosen and then modified under the influence of the potential field until an appropriate path is found. By considering the entire path, the problem of being trapped in a local minimum is greatly reduced, allowing the method to be used for global planning. The algorithm was tried with success on many different realistic planning problems. By way of illustration, the algorithm is applied to a two-dimensional revolute manipulator, a mobile robot capable of translation only, and a mobile robot capable of both translation and rotation.>

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Crossref · 10.1109/robot.1989.100007

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Metadata

Authors
Warren, C. W.
Venue
Proceedings, 1989 International Conference on Robotics and Automation
Published
2003-01-07
Pages
316-321
ISSN
1050-4729 (ISSN-L)
OpenAlex
W2116966230
Cited by
370
References
2
FWCI
5.18
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Global path planning using artificial potential fields. https://app.cochat.ai/papers/10.1109/robot.1989.100007
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Warren, C. W. (2003). Global path planning using artificial potential fields. Proceedings, 1989 International Conference on Robotics and Automation. https://doi.org/10.1109/robot.1989.100007

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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.