基于改进海鸥优化算法的多场景多障碍无人机三维路径规划OA

The goal of UAV 3D path planning is to plan an efficient and feasible flight path while avoiding obstacles and meeting constraint conditions.Therefore,an improved seagull optimization algorithm(TP-SOA)is proposed to solve the three-dimensional path planning prob-lem of unmanned aerial vehicles(UAVs)in multiple scenarios and obstacles,taking into account the widespread application and computation-al complexity of UAV path planning.Firstly,a nonlinear convergence factor is introduced to adjust the iteration process of the seagull optimiza-tion algorithm,allowing individuals to maintain a high degree of randomness in the early stages of the algorithm and converge quickly in the lat-er stages;Secondly,the Levi flight mechanism is adopted in the search method to expand the effective area of local search and improve the in-dividual's ability to jump out of local optima;Finally,an individual optimal strategy is introduced to increase the learning process of individu-als on the historical optimal individual positions and improve the optimization performance of the algorithm.The simulation experiment results show that TP-SOA can plan high-quality paths in complex multi obstacle scenarios,with higher convergence accuracy and stability compared to the control algorithm,demonstrating significant advantages.