基于DDPG的单目无人机避障算法

提出了一种基于单目相机的小型多旋翼无人机的连续避障策略。所提出的方法包括深度估计和导航决策两个模块。其中，在深度估计模块采用条件对抗网络对无人机采集得到的RGB图片进行训练预处理，在导航决策模块采用深度确定性策略梯度(DDPG)算法实现无人机的连续避障。在此基础上，对DDPG中的Actor网络进行改进，通过使用多模态网络代替原有策略网络，从而抑制无人机飞行震动，提高避障能力。最后，在Airsim仿真环境中进行测试，实验表明所提算法模型经过训练能够使无人机成功躲避障碍物并到达指定目标点，与原有算法相比避障轨迹得到明显改善。

Obstacle Avoidance Algorithm for Monocular UAV Based on DDPG

A continuous obstacle avoidance strategy for a small multi-rotor UAV based on a monocular camera is proposed. The proposed method includes two modules of depth estimation and navigation decision. Among them, the conditional confrontation network is used in the depth estimation module to train and preprocess the RGB images collected by the UAV, and the deep deterministic policy gradient (DDPG) algorithm is used in the navigation decision module to realize the continuous obstacle avoidance of the UAV. On this basis, the Actor network in DDPG is improved, and the multi-modal network is used to replace the original policy network, thereby suppressing the flight vibration of the UAV and improving the obstacle avoidance ability. Finally, the test is carried out in the Airsim simulation environment. The experiment shows that the proposed algorithm model can make the UAV successfully avoid obstacles and reach the designated target point after training. Compared with the original algorithm, the obstacle avoidance trajectory has been significantly improved.