基于贝叶斯决策的无人机飞行路径自动规划方法

传统无人机飞行路径自动规划方法无法获取全部障碍物信号,使无人机飞行不能达到避障效果,导致飞行路线规划效果较差;为此提出基于贝叶斯决策的无人机飞行路径自动规划方法;无人机飞行路径自动规划硬件模块包含自动规划模块、动画演示模块、地图导航模块和数据导出模块,自动规划模块负责控制无人机飞行;动画演示模块使用240PRO型号的LEWITT声卡,为展示飞机飞行路线提供声音;LS-TM8N地图导航模块通过串口将射频信号发送到天线的输入端,再由数据导出模块导出并保存相关数据;基于贝叶斯决策原理,结合贝叶斯元胞蚁群算法,计算贝叶斯先验概率和后验概率,规划无人机飞行路径,获取最优路径;实验结果表明,该方法遇到静态障碍物捕获的避障信号在-28～30mV范围内波动,动态障碍物捕获的避障信号在-27～30 mV范围内波动,与实际障碍物信号波动范围一致,避障效果较优.

An automatic flight path planning method for unmanned aerial vehicle based on Bayesian decision

Traditional automatic flight path planning methods for UAVs cannot obtain all obstacle signals, making UAV flight unable to achieve obstacle avoidance effects, resulting in poor flight path planning effects. To this end, an automatic UAV flight path planning method based on Bayesian decision-making is proposed. The automatic planning module of the drone flight path includes an automatic planning module, an animation demonstration module, a map navigation module and a data export module. The automatic planning module is responsible for controlling the flight of the drone; the animation demonstration module uses the 240PRO model LEWITT sound card to show the flight path of the aircraft Provide sound; LS-TM8N map navigation module sends the radio frequency signal to the input end of the antenna through the serial port, and then the data export module exports and saves the relevant data. Based on the Bayesian decision-making principle, combined with the Bayesian cell ant colony algorithm, the Bayesian prior probability and the posterior probability are calculated, the flight path of the UAV is planned, and the optimal path is obtained. The experimental results show that the obstacle avoidance signal captured by the method encounters static obstacles fluctuates in the range of -28-30mV, and the obstacle avoidance signal captured by dynamic obstacles fluctuates in the range of -27-30mV, which is consistent with the actual obstacle signal fluctuation range. , The obstacle avoidance effect is better.