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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yiwei Na & Yulong Li & Danqiang Chen & Yongming Yao & Tianyu Li & Huiying Liu & Kuankuan Wang, 2023. "Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization," Sustainability, MDPI, vol. 15(16), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12101-:d:1212434
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    References listed on IDEAS

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    1. Yinyin Wang & Lokeshkumar Kumar & Vijayanandh Raja & Hussein A. Z. AL-bonsrulah & Naveen Kumar Kulandaiyappan & Ashok Amirtharaj Tharmendra & Nagaraj Marimuthu & Mohammed Al-Bahrani, 2022. "Design and Innovative Integrated Engineering Approaches Based Investigation of Hybrid Renewable Energized Drone for Long Endurance Applications," Sustainability, MDPI, vol. 14(23), pages 1-48, December.
    2. Yafei Li & Minghuan Liu, 2022. "Path Planning of Electric VTOL UAV Considering Minimum Energy Consumption in Urban Areas," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
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    Cited by:

    1. Jue Wang & Bin Ji & Qian Fu, 2024. "Soft Actor-Critic and Risk Assessment-Based Reinforcement Learning Method for Ship Path Planning," Sustainability, MDPI, vol. 16(8), pages 1-16, April.
    2. Wen Qiu & Xun Shao & Hiroshi Masui & William Liu, 2024. "Optimizing Drone Energy Use for Emergency Communications in Disasters via Deep Reinforcement Learning," Future Internet, MDPI, vol. 16(7), pages 1-18, July.

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