• 论文 •

### 复杂低空物流无人机路径规划

1. 南京航空航天大学 民航学院, 南京 211106
• 收稿日期:2019-08-26 发布日期:2020-07-18
• 通讯作者: 张洪海 E-mail:honghaizhang@nuaa.edu.cn
• 作者简介:张启钱 男,博士,副研究员,硕士生导师。主要研究方向:交通运输规划与管理。
张洪海 男,博士,教授,博士生导师。主要研究方向:交通运输规划与管理。
• 基金资助:
国家自然科学基金（61573181，71971114）；南京航空航天大学研究生创新基地（实验室）开放基金（kfjj20180726）

### Path planning for logistics UAV in complex low-altitude airspace

ZHANG Qiqian, XU Weiwei, ZHANG Honghai, ZOU Yiyuan, CHEN Yutong

1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
• Received:2019-08-26 Published:2020-07-18
• Supported by:
National Natural Science Foundation of China (61573181,71971114); Nanjing University of Aeronautics and Astronautics Graduate Innovation Base (Lab) Open Fund (kfjj20180726)

Abstract: To solve the problem of path planning for logistics UAV in the complex low-altitude airspace, internal and external restrictions such as airspace environment and transportation tasks were considered. Taking minimize flight time, energy consumption and path risk as the objective function, the multi-restricted transportation path planning model of logistics UAV was established. To plan the path quickly, an improved heuristic algorithm was designed. The grid method was used to model the environment. The performance constraints of UAV were introduced to ensure that UAV can follow the path. To solve the existing problems of the original algorithm and indicate the characteristics of logistics UAV air transportation, the concepts of grid risk and cargo weight penalty coefficient were introduced, and flight time and energy consumption were calculated to improve the obstacle avoidance ability and reduce the cost. The dynamic weighting method was used to assign the weight of the function to match the efficiency and accuracy of the algorithm. In order to delete redundant path points and ensure smooth flight, bidirectional cross judgment method was used to optimize and smooth the original path. In order to verify the effectiveness of the model and the algorithm, the results of four algorithms were compared. Meanwhile, the influence of grid length and cost weight on planning results was analyzed. With the constraints of the assumed environment and UAV performance, the study results indicate that, compared with the original algorithm, the proposed algorithm ensures the flight safety of logistics UAV with less energy consumption, and reduces the flight time from 406 s to 386 s, which is reduced by 5%. The number of flight path points is 129 and the grid risk is 11.69, which reduces the number of attitude changes and ensures the safety of transportation. When the grid length is 5 m and the cost weight is 0.4, 0.1 and 0.5, the path planned by the proposed algorithm is optimal.