Research On UAV Route Planning Technology Based On Improved Whale Optimizaion Algorithm

With the rapid development of science and technology,the development technology of UAVs has been greatly improved,and UAVs can replace humans to complete various complex tasks,which are popular in both military and civilian markets.In recent years,the application scenarios of UAVs have expanded in a diversified trend,and the autonomous flight of UAVs has become one of the current popular research directions.Track planning technology is a key technology for UAVs to achieve autonomous flight and improve mission completion efficiency.In the face of increasingly complex flight environments,traditional trajectory planning algorithms are computationally intensive and poorly generalized,making it difficult to solve ideal trajectories.Therefore,the swarm intelligence algorithm with strong global search capability and wide engineering generality has become the current research hotspot of the trajectory planning technology.As a new type of swarm intelligence algorithm with simple structure and fewer optimization parameters,the whale optimization algorithm has been widely used in engineering and academic circles.This paper carries out research based on the whale optimization algorithm and the trajectory planning problem,with the aim of solving the UAV2 D and 3D trajectory planning problems,and improving the whale optimization algorithm to achieve efficient solution of the trajectory planning problem.The main work of this paper is as follows.(1)Research on UAV two-dimensional trajectory planning based on improved whale optimization algorithm: Based on the analysis of the defects of the original whale optimization algorithm such as low initial population diversity and imbalance between global and local search of the algorithm,an improved whale optimization algorithm is proposed to solve the UAV two-dimensional trajectory planning problem.The algorithm increases the initial population diversity by introducing a backward learning mechanism and a nonlinear convergence factor strategy,and improves the balance and search ability between global search and local exploitation during the iterative computation of the algorithm.The improved algorithm is compared with other common swarm intelligence algorithms on benchmark test functions,and experiments show that the convergence performance of the improved algorithm is improved.The experiments show that the improved whale optimization algorithm can solve the trajectory with shorter length,fewer inflection points and higher stability than the original algorithm.(2)A multi-strategy fusion whale optimization algorithm for UAV 3D trajectory planning:In the face of solving the UAV 3D trajectory planning problem with higher degrees of freedom and more complex constraints,the original whale optimization algorithm is prone to fall into local optimality,and a multi-strategy fusion whale optimization algorithm is proposed.The multi-strategy fused whale optimization algorithm further improves the quality of the initial solution of the algorithm by chaotic mapping combined with backward learning;further improves the balance and search ability between global and local search of the algorithm by inertia weights and nonlinear convergence factor cooperation;and enhances the ability of the algorithm to jump out of the local optimum at a later stage by adding a polynomial variation strategy to the optimal individual.The improved algorithm is compared with other algorithms in benchmarking function comparison experiments,which show that the multi-strategy improvement approach can significantly improve the convergence performance and stability of the whale optimization algorithm.Finally,the UAV 3D mission space model is established and the B spline curve method is introduced to smooth the trajectory.The 3D trajectory planning simulation experiments show that the multi-strategy fused whale optimization algorithm can find trajectories with smaller cost,shorter flight paths and smoother trajectories than the preimprovement algorithm,and has certain superiority in solving 3D trajectory planning problems.