Research On Autonomous Pursuit And Obstacle Avoidance Of UAV Based On Multi-sensor Information Fusion

Nowadays,UAVs(Unmanned Aerial Vehicles)have received more and more attention in the military and civilian fields,and their autonomy and controllability are mainly reflected in the ability to autonomously complete scheduled tasks.In order to complete the mission,the UAVs need to ensure its own safety.It needs to obtain the accurate position and status information of the target and obstacles,and then to plan a safe path from the current position to the target position in a complex space,so that at has the ability to pursue the target autonomously and ability to avoid obstacles autonomously.The main research contents are as follows:1.The key issues in airborne multi-sensor information fusion are studied.In order to improve the tracking accuracy of the observation system,a new distributed heterogeneous multi-sensor information fusion method is proposed,and an interactive multi-model based on the fifth-order Cubature Kalman filter with covariance crossover algorithm is proposed.The target motion model and the measurement model of different sensors are studied,and different nonlinear filtering algorithms are studied.The filtering performance of each algorithm is compared through simulation experiments,and then an improved heterogeneous multi-sensor information fusion method is proposed.The algorithm proposed in this article is effective.2.The autonomous pursuit and obstacle avoidance problems of UAVs are studied.In order to solve the problems in the artificial potential field method,an improved artificial potential field method based on the joint control of convergence factor and UAVs quality is proposed.First,the artificial potential field method for moving targets and obstacles is studied;secondly,the flight safety airspace and overload constraints of UAVs are studied,and simulation experiments are performed to verify them;finally,for the problem of the artificial potential field method of different planning powers for different quality UAVs and the unclear physical meaning of virtual forces proposed an improved method,and the effectiveness of the algorithm proposed in this paper was verified through simulation experiments.3.The problem of setting the safe airspace of drones is studied.In order to solve the problem of fixing the safe airspace of drones,an adaptive setting method of safe airspace based on target threat assessment is proposed.First,this paper uses fuzzy pattern discrimination and cloud theory methods to evaluate the qualitative and quantitative attributes of the target respectively,and uses the analytic hierarchy process to weight different levels of target attributes to obtain the quantitative value of the target threat degree;secondly,aiming at the problem of fixed safe airspace of UAVs,this paper proposes an improved method based on target threat assessment.Finally,simulation experiments verify the effectiveness and adaptability of the algorithm proposed in this paper.