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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 and Peng, Yonghong (2018) A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles. Complexity, 2018. pp. 1-17. ISSN 1076-2787

Item Type: Article

Abstract

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.

(PDF) A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles. Available from: https://www.researchgate.net/publication/328765418_A_New_Dynamic_Path_Planning_Approach_for_Unmanned_Aerial_Vehicles [accessed Nov 20 2018].

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More Information

Depositing User: Yonghong Peng

Identifiers

Item ID: 10176
Identification Number: https://doi.org/10.1155/2018/8420294
ISSN: 1076-2787
URI: http://sure.sunderland.ac.uk/id/eprint/10176
Official URL: https://doi.org/10.1155/2018/8420294

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Catalogue record

Date Deposited: 20 Nov 2018 14:56
Last Modified: 18 Dec 2019 16:07

Contributors

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

University Divisions

Faculty of Technology
Faculty of Technology > FOT Executive

Subjects

Computing > Data Science
Computing > Artificial Intelligence

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