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A new dynamic path planning approach for unmanned aerial vehicles

Huang, Chenxi; Lan, Yisha; Liu, Yuchen; Zhou, Wen; Pei, Hongbin; Yang, Longzhi; Cheng, Yongqiang; Hao, Yongtao; Peng, Yonghong


Chenxi Huang

Yisha Lan

Yuchen Liu

Wen Zhou

Hongbin Pei

Longzhi Yang

Yongqiang Cheng

Yongtao Hao

Yonghong Peng


Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm.


Huang, C., Lan, Y., Liu, Y., Zhou, W., Pei, H., Yang, L., …Peng, Y. (2018). A new dynamic path planning approach for unmanned aerial vehicles. Complexity, 2018, Article 8420294.

Journal Article Type Article
Acceptance Date Oct 21, 2018
Online Publication Date Nov 5, 2018
Publication Date Nov 5, 2018
Deposit Date Aug 9, 2019
Publicly Available Date Aug 9, 2019
Journal Complexity
Print ISSN 1076-2787
Electronic ISSN 1099-0526
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2018
Article Number 8420294
Keywords Multidisciplinary
Public URL
Publisher URL


Published article (5.9 Mb)

Copyright Statement
© 2018 Chenxi Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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