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<p>This paper addresses the trajectory planning problem for a quadrotor unmanned aerial vehicle (UAV) carrying a suspended payload in complex 3D environments. Traditional methods often result in trajectories with poor smoothness, low tracking accuracy, and significant payload swing, which compromise both safety and efficiency. To overcome these limitations, we propose a novel trajectory planning framework that integrates a 3D A* global path search with an intelligently tuned optimization function. First, an initial collision-free path is generated using the 3D A* algorithm. Then, key reference points are extracted by simplifying the initial path using the Douglas–Peucker algorithm. A hybrid optimization function—minimizing both jerk and snap—is constructed and adaptively tuned using the particle swarm optimization (PSO) algorithm to generate smooth, energy-efficient trajectories that comply with the system’s dynamic constraints. Simulation results demonstrate that the proposed method achieves superior payload swing suppression compared to B-spline and minimum-snap baselines albeit with slightly higher energy consumption and tracking error. This trade-off highlights the method’s effectiveness in prioritizing swing reduction for enhanced transport stability.</p>

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Trajectory Planning for Quadrotor UAV with Suspended Payload via PSO-Tuned Hybrid Polynomial Optimization

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Xinhai Wang,
  • Lu Lu,
  • Shikang Zhou,
  • Yunhe Meng

摘要

This paper addresses the trajectory planning problem for a quadrotor unmanned aerial vehicle (UAV) carrying a suspended payload in complex 3D environments. Traditional methods often result in trajectories with poor smoothness, low tracking accuracy, and significant payload swing, which compromise both safety and efficiency. To overcome these limitations, we propose a novel trajectory planning framework that integrates a 3D A* global path search with an intelligently tuned optimization function. First, an initial collision-free path is generated using the 3D A* algorithm. Then, key reference points are extracted by simplifying the initial path using the Douglas–Peucker algorithm. A hybrid optimization function—minimizing both jerk and snap—is constructed and adaptively tuned using the particle swarm optimization (PSO) algorithm to generate smooth, energy-efficient trajectories that comply with the system’s dynamic constraints. Simulation results demonstrate that the proposed method achieves superior payload swing suppression compared to B-spline and minimum-snap baselines albeit with slightly higher energy consumption and tracking error. This trade-off highlights the method’s effectiveness in prioritizing swing reduction for enhanced transport stability.