华东师范大学学报(自然科学版) ›› 2022, Vol. 2022 ›› Issue (6): 54-67.doi: 10.3969/j.issn.1000-5641.2022.06.007

• 计算机科学 • 上一篇    下一篇

基于改进人工势场法的无人机三维避障

周兰凤(), 孔明月   

  1. 上海应用技术大学 计算机科学与信息工程学院, 上海 201418
  • 收稿日期:2021-08-24 出版日期:2022-11-25 发布日期:2022-11-22
  • 作者简介:周兰凤, 女, 副教授, 主要研究方向为地形可视化、路径规划、大数据等. E-mail: lfzhouhit@163.com

3D obstacle-avoidance for a unmanned aerial vehicle based on the improved artificial potential field method

Lanfeng ZHOU(), Mingyue KONG   

  1. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China
  • Received:2021-08-24 Online:2022-11-25 Published:2022-11-22

摘要:

针对无人机在三维环境中寻求从起始位置到目标位置的一条最优、安全且能避开所有障碍物的路径, 提出了一种结合正六边形导向法改进的人工势场法, 用来解决传统人工势场法中障碍物附近目标不可达和局部极小值问题. 首先, 在排斥势场函数中加入距离修正因子, 解决目标不可达问题; 其次, 提出了一种正六边形导向法来改进局部极小值问题, 该方法可以在无人机陷入局部极小值区域时, 判断所处环境并选择适当的规划方法, 引导无人机逃离局部极小值区域; 再次, 在Matlab平台上进行了三维建模仿真, 同时考虑到多种复杂障碍物的场景, 结果表明了该方法在保留了原本算法优势的同时, 在无人机实时路径规划中更加有效和可行; 最后, 在真实环境中演示了所提出的方法, 实验结果表明了该方法的可行性和有效性.

关键词: 无人机, 避障, 三维环境, 人工势场法, 局部极小值

Abstract:

This paper aims to address the challenge of seeking an optimal safe path for a UAV (unmanned aerial vehicle) from an initial position to a target position, while avoiding all obstacles in a three-dimensional environment. An improved APF (artificial potential field) method combined with the regular hexagon guidance method is proposed to solve unreachable and local minimum problems near obstacles as observed with traditional artificial potential field methods. First, we add a distance correction factor to the repulsive potential field function to solve problems associated with unreachable targets. Then, a regular hexagon-guided method is proposed to improve the local minimum problem. This method can judge the environment when the UAV is trapped in a local minimum point or trap area and select the appropriate planning method to guide the UAV to escape from the local minimum area. Then, 3D modeling and simulation were carried out via Matlab, taking into account a variety of scenes involving complex obstacles. The results show that this method has good feasibility and effectiveness in real-time path planning of UAVs. Lastly, we demonstrate the performance of the proposed method in a real environment, and the experimental results show that the proposed method can effectively avoid obstacles and find the optimal path.

Key words: unmanned aerial vehicle, obstacle avoidance, three-dimensional environment, artificial potential field method, local minimum

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