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Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
For the problem of mobile robot’s path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed.
First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop.
Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone.
The process of optimization of ACO is divided into two phases.
In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner.
In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path.
Finally, some simulation experiments and mobile robot environment experiments are done.
The experiment results verify that the method has stronger stability and environmental adaptability.
American Psychological Association (APA)
Chen, Guoliang& Liu, Jie. 2019. Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129354
Modern Language Association (MLA)
Chen, Guoliang& Liu, Jie. Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1129354
American Medical Association (AMA)
Chen, Guoliang& Liu, Jie. Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129354
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129354