基于LiDAR的无人机强化学习导航
LiDAR-based drone navigation with reinforcement learning
AI总结：
本研究基于旋转LiDAR传感器数据，采用PPO算法开发自主无人机控制系统，结合Python仿真器与ROS集成的Gazebo环境测试，并在Nvidia Jetson Nano硬件上通过真实场景验证，实现森林避障飞行。
摘要：
强化学习在机器人控制领域日益重要，仿真在其中发挥关键作用。在无人机（UAVs，drones）领域，涉及该方法的科学论文数量也在增加。在本研究中，开发了一个自主无人机控制系统，使其能够向前飞行（根据其坐标系）并基于旋转LiDAR传感器的数据穿越森林中遇到的树木。为此，采用了强化学习（RL）的实例——近端策略优化（PPO）算法进行系统构建。同时，使用Python语言开发了专用仿真器。此外，还利用与机器人操作系统（ROS）集成的Gazebo环境对所得控制算法进行测试。最终，所提方案在Nvidia Jetson Nano eGPU上实现，并在真实测试场景中验证。测试期间，无人机成功完成设定任务，能够重复性地避开树木并飞越森林。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
