With the continuous development of UAV technology, UAV has been widely used in various industries. In the flight process of UAV, UAV often changes the given path because of obstacles (including static nonliving body and moving living body). According to the properties of obstacles and the characteristics of UAV, standard Kalman filter is used for nonmaneuvering targets, and sigma point Kalman filter is used for maneuvering targets. In the aspect of obstacle avoidance, the minimum search method is used to get the initial population of local programming. Then, the improved genetic algorithm is run. Combined with the predicted obstacle features, the local planning path can be obtained. Finally, the local planning path and global planning path are combined to generate the planning path with new obstacles. At the end of the paper, the obstacle avoidance strategies of static and moving obstacles are simulated. The simulation results show that this method has fast convergence speed and good feasibility and can flexibly deal with the obstacle avoidance and local path planning of various new obstacles.

中文翻译：

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一种无人机避障方法研究

随着无人机技术的不断发展，无人机已广泛应用于各个行业。在无人机的飞行过程中，无人机经常会因为障碍物（包括静止的无生命体和移动的生命体）而改变给定的路径。根据障碍物的性质和无人机的特点，非机动目标采用标准卡尔曼滤波，机动目标采用西格玛点卡尔曼滤波。在避障方面，采用最小搜索法得到局部编程的初始种群。然后，运行改进的遗传算法。结合预测的障碍物特征，可以获得局部规划路径。最后，结合局部规划路径和全局规划路径，生成具有新障碍物的规划路径。在论文的最后，模拟静态和移动障碍物的避障策略。仿真结果表明，该方法收敛速度快，可行性好，能够灵活应对各种新障碍物的避障和局部路径规划。