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As modern technology continues to advance, drones and their applications have attracted more and more attention. What is more efficient to achieve the trajectory planning and optimization of UAVs has become more and more important. Particle swarm algorithm is widely used in the field of UAVs, and it plays an important role in determining the path of UAVs. In this paper we will explore UAV trajectory planning based on particle swarm algorithm. The main work includes: Firstly, the dynamics attitude control about quadrotor UAV is studied. Subsequently, a comprehensive mathematical framework for Unmanned Aerial Vehicle (UAV) trajectory optimization is formulated, wherein the Particle Swarm Optimization (PSO) algorithm is employed to ascertain the optimal path planning solution. Ultimately, the efficacy of the algorithm, both prior to and following enhancement, is evaluated through the application of two standard benchmark functions.

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Autonomous Attitude Control and Trajectory Planning for UAV

  • 会议论文

  • 发表日期：

    2025年

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  • Yinghuang Liu,
  • Xizhe Tian,
  • Bowen Xue,
  • Qijin Yuan,
  • Zhonmin Qiu,
  • Jian Wang

摘要

As modern technology continues to advance, drones and their applications have attracted more and more attention. What is more efficient to achieve the trajectory planning and optimization of UAVs has become more and more important. Particle swarm algorithm is widely used in the field of UAVs, and it plays an important role in determining the path of UAVs. In this paper we will explore UAV trajectory planning based on particle swarm algorithm. The main work includes: Firstly, the dynamics attitude control about quadrotor UAV is studied. Subsequently, a comprehensive mathematical framework for Unmanned Aerial Vehicle (UAV) trajectory optimization is formulated, wherein the Particle Swarm Optimization (PSO) algorithm is employed to ascertain the optimal path planning solution. Ultimately, the efficacy of the algorithm, both prior to and following enhancement, is evaluated through the application of two standard benchmark functions.