| Citation: | ZHANG Qiqian, XU Weiwei, ZHANG Honghai, et al. Path planning for logistics UAV in complex low-altitude airspace[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1275-1286. doi: 10.13700/j.bh.1001-5965.2019.0455(in Chinese)
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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.
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