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3D Spatial Path Planning Based on Improved Particle Swarm Optimization

Author

Listed:
  • Junxia Ma

    (College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China)

  • Zixu Yang

    (Anhui Houpu Digital Technology Co., Ltd., Hefei 230000, China)

  • Ming Chen

    (School of Mathematics and Computer Science, Tongling University, Tongling 244061, China)

Abstract

Three-dimensional path planning is critical for the successful operation of unmanned aerial vehicles (UAVs), automated guided vehicles (AGVs), and robots in industrial Internet of Things (IIoT) applications. In 3D path planning, the standard Particle Swarm Optimization (PSO) algorithm suffers from premature convergence and a tendency to fall into local optima, leading to significant deviations from the optimal path. This paper proposes an improved PSO (IPSO) algorithm that enhances particle diversity and randomness through the introduction of logistic chaotic mapping, while employing dynamic learning factors and nonlinear inertia weights to improve global search capability. Experimental results demonstrate that IPSO outperforms traditional methods in terms of path length and computational efficiency, showing potential for real-time path planning in complex environments.

Suggested Citation

  • Junxia Ma & Zixu Yang & Ming Chen, 2025. "3D Spatial Path Planning Based on Improved Particle Swarm Optimization," Future Internet, MDPI, vol. 17(9), pages 1-21, September.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:406-:d:1743409
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    References listed on IDEAS

    as
    1. Pejman A. Karegar & Duaa Zuhair Al-Hamid & Peter Han Joo Chong, 2024. "Deep Reinforcement Learning for UAV-Based SDWSN Data Collection," Future Internet, MDPI, vol. 16(11), pages 1-14, October.
    2. Laura Vaccari & Antonio Maria Coruzzolo & Francesco Lolli & Miguel Afonso Sellitto, 2024. "Indoor Positioning Systems in Logistics: A Review," Logistics, MDPI, vol. 8(4), pages 1-31, December.
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