Path Planning of UAVs Based on Collision Probability and Kalman Filter

Abstract

For clusters of UAVs, the scale and density of the cluster determine its ability to solve the task. With the increasing density of aerial vehicles, effectively planning a reasonable flight path and avoiding conflicts among flight paths have become key problems for UAV clusters. The traditional control method is to detect potential conflicts through radar monitoring or location reporting in the air and to then change the flight path, including the height, heading, and speed, through manual instruction. To solve the problem of path conflicts for UAV clusters, a method for calculating the collision probabilities of UAVs is established under the constraints of mission space and the number of UAVs. In cluster flight mode, automatic tracking and prediction of UAV cluster tracks are implemented to avoid path conflicts in clusters. In addition, to address the inconsistency problem because of noise caused by the state information of multi UAV communication under a dynamic environment, a state estimation method is proposed based on the Kalman algorithm. To achieve aircraft track planning, cluster state prediction and collision probability are eventually calculated to avoid the clusters of formation UAVs conflicting on paths during flight. Finally, the simulation results verify the validity and effectiveness of the proposed method in multi UAV formation flight planning.