Research On Cooperative Route Planning Technology Of UAVs Formation

With the rapid development of science and technology,unmanned aerial vehicle technology has attracted wide attention.Due to the complexity of the mission environment and the limited effectiveness of the single unmanned aerial vehicle,formation coordinated flight has become one of the future development directions.Collaborative route planning is a key technology to ensure formation autonomous flight and has important research significance.Based on the existing literature,this paper focuses on the application of particle swarm optimization and gaussian pseudospectral method in formation cooperative route planning.First of all,the definition of route planning is elaborated,and according to the uav's own performance,restrictions such as turning Angle,pitch Angle and flight altitude are set,so as to analyze the possible threat constraints during flight and the cooperative relationship between uav formation space and time.According to the above constraints,the comprehensive cost evaluation function is established and the route planning model is established.Secondly,the shortcomings of particle swarm optimization in cooperative route planning are analyzed,and an improved pso is proposed.The linearization of the parameters can realize the balance between the inertial ability of particles and the learning behavior.Chaos initialization is introduced to improve the distribution quality of initial value.The design replaces the strategy and speed regulation mechanism to improve the convergence speed.The effectiveness of the improved particle swarm optimization is demonstrated by simulation experiments.Then,in order to obtain more reference information of uav formation flight quickly,a cooperative route planning based on improved gauss pseudospectral method is proposed.Based on the improved particle swarm optimization proposed in this paper,the sensitivity of pseudospectral method to initial value is solved.In addition,the multi-stage decomposition design is carried out to accelerate the convergence speed,and the superiority of the improved algorithm is verified through the comparison of simulation experiments.Finally,Pixhawk flight control was selected and a hardware experimental platform was built.The flight formation test was conducted according to the route planning algorithm proposed in this paper,and the tracking control of route planning results wascompleted.The actual feasibility of the algorithm was illustrated by analyzing flight data.