Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization

Yiwei Na, Yulong Li, Danqiang Chen, Yongming Yao (), Tianyu Li, Huiying Liu and Kuankuan Wang
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Yiwei Na: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
Yulong Li: Beijing Institute of Space Launch Technology, Beijing 100076, China
Danqiang Chen: Aviation College, Aviation University of Air Force, Changchun 130022, China
Yongming Yao: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
Tianyu Li: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
Huiying Liu: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
Kuankuan Wang: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

Sustainability, 2023, vol. 15, issue 16, 1-16

Abstract: In order to enhance the energy efficiency of unmanned aerial vehicles (UAVs) during flight operations in mountainous terrain, this research paper proposes an improved particle swarm optimization (PSO) algorithm-based optimal energy path planning method, which effectively reduces the non-essential energy consumption of UAV during the flight operations through a reasonable path planning method. First, this research designs a 3D path planning method based on the PSO optimization algorithm with the goal of achieving optimal energy consumption during UAV flight operations. Then, to overcome the limitations of the classical PSO algorithm, such as poor global search capability and susceptibility to local optimality, a parameter adaptive method based on deep deterministic policy gradient (DDPG) is introduced. This parameter adaptive method dynamically adjusts the main parameters of the PSO algorithm by monitoring the state of the particle swarm solution set. Finally, the improved PSO algorithm based on parameter adaptive improvement is applied to path planning in mountainous terrain environments, and an optimal energy-consuming path-planning algorithm for UAVs based on the improved PSO algorithm is proposed. Simulation results show that the path-planning algorithm proposed in this research effectively reduces non-essential energy consumption during UAV flight operations, especially in more complex terrain scenarios.

Keywords: deep reinforcement learning; optimal energy consumption; parameter adaption; path planning; particle swarm algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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