Multi-Scene and Multi-Obstacle UAV 3D Path Planning Based on Improved Seagull Optimization Algorithm
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.
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