UAV track planning based on evolution algorithm in embedded system

摘要

The embedded controller is small in size and powerful in computing power, and can quickly complete the related processing and calculation of flight attitude and track planning data. Wireless data transmission can realize a long-distance data transmission between the aircraft command center and the airborne control platform. Based on the data fusion algorithm of the Kalmanz filter, the data collected by multiple sensors can be integrated, which can effectively reduce the measurement noise amplitude. At the same time, the cumulative error of a single sensor is reduced. In order to solve the problems of ant colony algorithm in case it is easy to fall into the local extremum and the convergence speed is slow, an improved ant colony algorithm for 3D navigation of unmanned aerial vehicles is proposed for trace planning. This study divides the three-dimensional track planning into two parts based on the improved ant colony algorithm for two-dimensional plane planning and height planning. Geometric optimization methods to enhance the guidance of ant search are used. According to the distance and height constraints between the track point and the threat source, the altitude of the track points to plan the 3D track of the drone is calculated and adjusted. At the same time, the adaptive parameter adjustment method is used to improve the ant colony search ability and the interaction ability between individuals, and effectively get rid of the situation to avoid to falling into a local optimum. In addition, the index function is established and the path is smoothed. Simulation results show that the proposed improved algorithm cannot only safely avoid threats in the three-dimensional environment, but also has the ability to find the optimal solution and the convergence speed is better than the original algorithm. (C) 2020 Elsevier B.V. All rights reserved.