This paper proposes a solution for the path following problem of a quadrotor vehicle based on deep reinforcement learning theory. Three different approaches implementing the Deep Deterministic Policy Gradient algorithm are presented. Each approach emerges as an improved version of the preceding one. The first approach uses only instantaneous information of the path for solving the problem. The second approach includes a structure that allows the agent to anticipate to the curves. The third agent is capable to compute the optimal velocity according to the path’s shape. A training framework that combines the tensorflow-python environment with Gazebo-ROS using the RotorS simulator is built. The three agents are tested in RotorS and experimentally with the Asctec Hummingbird quadrotor. Experimental results prove the validity of the agents, which are able to achieve a generalized solution for the path following problem.

本文提出了一种基于深度强化学习理论的四旋翼飞行器路径跟踪问题的解决方案。提出了三种实现深度确定性策略梯度算法的不同方法。每种方法都是前一种方法的改进版本。第一种方法仅使用路径的瞬时信息来解决问题。第二种方法包括允许代理预测曲线的结构。第三代理能够根据路径的形状计算最佳速度。建立了一个使用RotorS模拟器将tensorflow-python环境与Gazebo-ROS相结合的训练框架。这三种试剂在RotorS中进行了测试，并通过Asctec Hummingbird四旋翼进行了实验。实验结果证明了这些药剂的有效性，