School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
School of Microelectronics, Jiangsu Vocational College of Information Technology, Wuxi 214153, China.
Sensors (Basel). 2023 Jun 25;23(13):5896. doi: 10.3390/s23135896.
This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.
本文提出了一种改进的 3D-Vector Field Histogram（3D-VFH）算法，用于多旋翼无人机（UAV）在受限环境下的自主飞行和局部避障。首先，该方法采用基于极坐标的目标点坐标系来转换点云数据，考虑到远距离点云信息对 UAV 局部避障没有影响。这使得 UAV 能够有效地利用障碍物信息进行避障，并提高算法的实时性能。其次，采用滑动窗口算法估计 UAV 的最优飞行路径并实现避障控制，从而在避障飞行过程中保持 UAV 的姿态稳定性。最后，进行了实验分析，结果表明，UAV 在避障飞行过程中具有良好的姿态稳定性，能够自主跟踪期望轨迹，并能够避开动态障碍物，实现精确避障。
