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Three-dimensional path planning with enhanced gravitational search algorithm for unmanned aerial vehicle

Published online by Cambridge University Press:  22 May 2024

Keming Jiao Open the ORCID record for Keming Jiao [Opens in a new window] ,
Jie Chen ,
Bin Xin ,
Li Li ,
Yifan Zheng  and
Zhixin Zhao
Keming Jiao
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
Jie Chen
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
Bin Xin*
Affiliation:
School of Automation, Beijing Institute of Technology, Beijing, China National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing, China
Li Li
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
Yifan Zheng
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
Zhixin Zhao
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai, China Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
*
Corresponding author: Bin Xin; Email: brucebin@bit.edu.cn
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Abstract

Path planning for the unmanned aerial vehicle (UAV) is to assist in finding the proper path, serving as a critical role in the intelligence of a UAV. In this paper, a path planning for UAV in three-dimensional environment (3D) based on enhanced gravitational search algorithm (EGSA) is put forward, taking the path length, yaw angle, pitch angle, and flight altitude as considerations of the path. Considering EGSA is easy to fall into local optimum and convergence insufficiency, two factors that are the memory of current optimal and random disturbance with chaotic levy flight are adopted during the update of particle velocity, improving the balance between exploration and exploitation for EGSA through different time-varying characteristics. With the identical cost function, EGSA is compared with seven peer algorithms, such as moth flame optimization algorithm, gravitational search algorithm, and five variants of gravitational search algorithm. The experimental results demonstrate that EGSA is superior to the seven comparison algorithms on CEC 2020 benchmark functions and the path planning method based on EGSA is more valuable than the other seven methods in diverse environments.

Keywords

unmanned aerial vehiclepath planninggravitational search algorithmchaos
Type
Research Article
Information
Robotica , First View , pp. 1 - 35
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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