Research On Path Planning Algorithm Of Low-altitude UAV In Densely Built-up Area

With the orderly opening of urban low-altitude airspace,the civil flight projects of low-altitude UAV densely built-up area are increasing day by day.Path planning is one of the key technologies for autonomous flight of UAV,the study of path planning in the densely built-up area is conducive to the development of UAV in the civil field.At present,the path planning methods are mainly applied to the environment with few obstacles or high altitude,there are few researches on UAV path planning in densely built-up areas,and most of the path planning algorithms have problems such as poor robustness and poor efficiency.Based on the above problems,this paper makes the following research:1)An improved artificial bee colony algorithm based on quantum strategy is proposed,which improves the searching ability and convergence speed of artificial bee colony algorithm in 2D path planning of UAV.By setting the moving step lengths to transform the nectar sources generation mode,the nectar sources can be connected and the effectiveness of the initial nectar sources can be enhanced.The sensitivity the pheromone model and was introduced to replace the traditional selection model to avoid the algorithm falling into local optimum.Quantum strategy was introduced in the onlookers phase to update the nectar source,which improved the convergence speed of the algorithm.Compared with the basic algorithm,the cost function standard deviation of the proposed algorithm is reduced by 60.00% and the number of convergent iterations is reduced by 52.50% in the simulation experiment of 2D static path planning in densely built-up area.The proposed algorithm has better robustness and planning efficiency,showing better robustness and planning efficiency.2)An improved artificial bee colony algorithm based on chaotic sequence is proposed to improve the optimization quality of artificial bee colony algorithm in 3D path planning.Based on the2 D path planning algorithm,the algorithm initializes the nectar source by using chaotic sequence to improve the feasibility and diversity of the nectar source.At the same time,the sensitivity and the pheromone model is introduced to improve the neighborhood search method of the nectar source and optimize the calculation effect.Compared with the basic artificial bee colony algorithm,the standard deviation of the cost function value of the improved artificial bee colony algorithm is reduced by47.17% and the number of convergent iterations is reduced by 54.62% in the analysis of static path planning in 3D densely built-up area,which achieves a better solution effect.The simulation analysis of four different missions of UAV in built-up areas further verifies the superiority of the proposed algorithm.3)A real-time path planning method suitable for densely built-up areas is presented.The feasibility and real-time performance of the improved algorithms are confirmed by the simulation experiments of the two improved artificial bee colony algorithms in 2D and 3D environments respectively.In the experiment,real-time path planning under different conditions is designed for 2D and 3D environments according to the emergence position of emergent threats.The experimental results show that the algorithm can solve the real-time path planning problem in a relatively short time in the face of sudden threats.