Reinforcement learning is of increasing importance in the field of robot control and simulation plays a~key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest.

翻译：强化学习在机器人控制领域日益重要，而仿真在这一过程中扮演着关键角色。在无人机领域，涉及该方法的科学论文数量也在不断增加。本文基于旋转LiDAR传感器的数据，构建了一个自主无人机控制系统，使其能够（根据自身坐标系）向前飞行并穿越森林中遇到的树木。本研究采用近端策略优化算法（一种强化学习方法）进行训练。为此，我们使用Python语言开发了定制仿真器，并结合机器人操作系统集成的Gazebo环境对最终控制算法进行了测试。最后，该方案被部署在Nvidia Jetson Nano嵌入式GPU上，并在真实场景中进行了验证。测试结果显示，无人机成功完成了设定任务，能够重复性地避开树木并穿越森林。