Research On Uav Autonomous Obstacle Avoidance Strategy Based On Fluid Disturbance Algorithm

Compared with the traditional UAV obstacle avoidance algorithm,the fluid perturbation algorithm based on the fluid potential field has the characteristics of generating obstacle avoidance paths that follow the shape of obstacles efficiently and reduce the probability of falling into a local minimum.It is used in UAV applications.The field has received extensive attention.How to design a reasonable and efficient UAV obstacle avoidance strategy for the complex three-dimensional flight environment and obtain an efficient,feasible and smooth obstacle avoidance curve has become the main research direction of UAV control.On the one hand,the existing traditional obstacle avoidance algorithms in two-dimensional space have been increased to three-dimensional space,which is one of the bottlenecks of current research.On the other hand,as a brand-new algorithm based on fluid potential field,the fluid disturbance algorithm has good experimental results in the current application simulation experiments.Based on this,this paper conducts related research on the autonomous obstacle avoidance strategy of UAV based on the fluid disturbance algorithm.The main work and innovation points of the full text are listed below.（1）This paper proposes a new obstacle avoidance algorithm based on fluid disturbance and improved potential field.First,according to the known obstacle edge information,a standard convex modeling method is adopted,and the obstacle is roughly covered by a standardized model to generate obstacle information in the current flight environment,which reduces the complexity of the algorithm.Then,the potential field of repulsion and gravitation generated by the threedimensional artificial potential field（APF）method is used to perform a global search.Finally,when the obstacle is close to the surface,the fluid disturbance potential field generated by the fluid disturbance algorithm is used to achieve high-precision obstacle avoidance.Experiments show that the combination of artificial potential field and fluid disturbance obstacle avoidance algorithm can have better response speed and accuracy in three-dimensional space.（2）This paper proposes a UAV obstacle avoidance algorithm based on sparrow optimization and improved fluid disturbance algorithm is proposed.First,after analyzing the mathematical principles of the original disturbance matrix,the tangential matrix is introduced and the three characteristics of the improved fluid disturbance algorithm are proved.After this modification,the number of paths generated by the algorithm increases,and the rapid convergence and global search of the sparrow search algorithm are used.Ability,among obstacle response coefficients and tangential component coefficients within a certain threshold,the optimal set of parameters is found through continuous iteration to obtain the three-dimensional optimal route.The simulation results show that the algorithm proposed in this paper can ensure the smoothness and efficiency of the generated path under the premise of obtaining the optimal obstacle avoidance curve in the complex three-dimensional flight environment,and can avoid the local minimum problem to a certain extent.（3）This paper proposes an obstacle avoidance strategy based on the combination of LGVF and fluid disturbance algorithm to solve the problem of UAV tracking and obstacle avoidance in a complex three-dimensional environment.By introducing the H component to improve the traditional LGVF to redefine the initial flow field,replacing the initial flow field definition method based on the set distance in the traditional fluid disturbance algorithm,so as to combine the improved LGVF with the fluid disturbance algorithm to achieve the purpose of tracking and avoiding obstacles at the same time.The simulation experiments mainly verify the effect of the algorithm on static obstacle and dynamic obstacle tracking and different target motion curves.The experiment shows that the algorithm proposed in this chapter has better obstacle avoidance and tracking effects.