In this study, we applied reinforcement learning based on the proximal policy optimization algorithm to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. The application of reinforcement learning through a real UAV has several limitations such as time and cost; thus, we used the Gazebo simulator to train a virtual quadrotor UAV in a virtual environment. As the reinforcement learning progressed, the mean reward and goal rate of the model were increased. Furthermore, the test of the trained model shows that the UAV reaches the goal with an 81% goal rate using the simple reward function suggested in this work.

中文翻译：

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具有静态障碍物的虚拟开放空间中无人飞行器的强化学习运动规划

在本研究中，我们应用基于近端策略优化算法的强化学习，在具有静态障碍物的开放空间中为无人机 (UAV) 执行运动规划。通过真正的无人机应用强化学习有几个限制，例如时间和成本；因此，我们使用 Gazebo 模拟器在虚拟环境中训练虚拟四旋翼无人机。随着强化学习的进展，模型的平均奖励和目标率增加。此外，训练模型的测试表明，无人机使用本工作中建议的简单奖励函数以 81% 的目标率达到目标。