An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

Joint Authors

Wang, Hong-Jun
Fu, Yong
Zhao, Zhuo-Qun
Yue, You-Jun

Source

Journal of Robotics

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-06-09

Country of Publication

Egypt

No. of Pages

8

Main Subjects

Mechanical Engineering

Abstract EN

The obstacle avoidance in path planning, a hot topic in mobile robot control, has been extensively investigated.

The existing ant colony algorithms, however, remain as drawbacks including failing to cope with narrow aisles in working areas, large amount of calculation, etc.

To address above technical issues, an improved ant colony algorithm is proposed for path planning.

In this paper, a new weighted adjacency matrix is presented to determine the walking direction and the narrow aisles therefore are avoided by redesigning the walking rules.

Also, the best ant and the worst ant are introduced for the adjustment of pheromone to facilitate the searching process.

The proposed algorithm guarantees that robots are able to find a satisfying path in the presence of narrow aisles.

The simulation results show the effectiveness of the proposed algorithm.

American Psychological Association (APA)

Wang, Hong-Jun& Fu, Yong& Zhao, Zhuo-Qun& Yue, You-Jun. 2019. An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance. Journal of Robotics،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1186980

Modern Language Association (MLA)

Wang, Hong-Jun…[et al.]. An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance. Journal of Robotics No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1186980

American Medical Association (AMA)

Wang, Hong-Jun& Fu, Yong& Zhao, Zhuo-Qun& Yue, You-Jun. An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance. Journal of Robotics. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1186980

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186980