Low Altitude Path Planning of Urban UAV Based on Improved Lion Swarm Optimization

In order to solve the problems of the intelligent optimization algorithm in the three-dimensional (3D) path planning of grid method, such as weak searching ability, many turns and large angles of the planned path. In response to these problems, we propose an improved lion swarm optimization (ILSO) algorithm. Firstly, on the basis of the lion swarm optimization (LSO), we firstly integrate the Cauchy mutation operator to perform disturbance mutation at the optimal solution position to enhance the ability to jump out of the local optimal solution. Secondly, for the problem of path planning, we introduce the valuation function to screen the node connections, so as to avoid blind search of the algorithm. Thirdly, we perform a quadratic planning operation on the planned path to increase the probability that the algorithm will obtain the optimal solution. At the same time, we smooth the planning result by using the cubic B-spline function. Finally, the simulation results show that the ILSO algorithm is compared with genetic algorithm, particle swarm optimization, whale optimization algorithm, lion swarm optimization. The path is shortened by 5.42%, the time is shortened by 17.14%, and the number of turns is reduced by 45.71% on average under different distance planning.