Research On Multi-UAV Cooperative And Hybrid Path Planning Method In Complex Environment

In the future informatization and network environment,the development of UAVs tends to be cluster technology,formation coordination,trajectory planning,autonomous decisionmaking.Whether in the field of research or market application,"swarm intelligence" has received extensive attention from domestic and foreign research scholars,and the autonomy of UAVs has become a research bottleneck in the field of UAV control.In view of the complexity and uncertainty of the mission environment,this thesis conducts an in-depth study on how to improve the autonomy of UAVs and the autonomous decision-making capabilities of UAV formations.Through the multi-UAVs cooperative target search and enclosing experiment in an unknown environment,the aim is to improve the autonomous capability and collaboration efficiency of the drone.Based on the research of UAV collaboration,the intelligent trajectory planning of the UAV formation has been completed in response to multiple threats in the complex dynamic environment,thereby improving the adaptability of the drone formation to the complex dynamic environment.The main contributions of this article are as follows:1.Aiming at the problem of UAV collaborative target search in uncertain environments,a method of multi-UAVs collaborative target search method based on dual attribute probability graph combined with improved Co-evolutionary Genetic Algorithm(Improved Coevolutionary Genetic Algorithm,ICEGA)is proposed.By improving the accuracy of UAV’s information perception of the environment and targets,the multi-UAVs collaborative target search and enclosing experiment was completed,aiming to improve the autonomy of UAVs and the collaboration efficiency and search accuracy.And further add flying obstacles and moving targets in the environment,design dynamic target escape strategies and enclosing models based on virtual forces,and use multiple UAVs to coordinate target enclosing.2.Optimize the obstacle avoidance strategy and node extension method of the classic RRT algorithm,and propose a bidirectional fast search random tree algorithm based on the greedy strategy,which improves the solution efficiency and formation stability of the current optimization algorithm in a complex obstacle environment.The proposed optimized node growth method greatly improves the optimization convergence speed,and combined with the adaptive step-length rolling detection method,it effectively improves the planning efficiency,track smoothness and track length performance of UAV formation obstacle crossing.The UAV formation model solves the defect that it is easy to fall into the local optimum,which greatly improves the survivability of the cluster formation.3.Based on the research of multi-UAVs collaboration and RRT algorithm.Aiming at complex dynamic environment,a hybrid trajectory planning algorithm based on the combination of fluid potential energy field disturbance model and improved IBi-RRT is proposed to realize UAV in complex dynamic environment.Route planning of the formation.First,construct the obstacle potential energy function and the formation potential energy function through the dynamic system mathematical model based on the fluid potential energy field;then combine the introduction of the IBi-RRT algorithm with adaptive step size to solve the dispersed streamline and convergent streamline progress of the disturbed flow field.Route planning,algorithm adaptive step length optimization of the flow field route rolling,can greatly improve the formation’s ability to avoid dynamic threats,and effectively improve the adaptive ability of the UAV cluster formation to the environment.