论文标题
通过静态障碍物的虚拟开放空间中无人驾驶飞机的加强学习进行运动计划
Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles
论文作者
论文摘要
在这项研究中，我们根据近端政策优化算法应用了加固学习，以在具有静态障碍的开放空间中为无人机（UAV）执行运动计划。通过真正的无人机进行加固学习的应用有几个限制，例如时间和成本；因此，我们使用凉亭模拟器在虚拟环境中训练虚拟四极管无人机。随着强化学习的进行，模型的平均奖励和目标率也会增加。此外，训练有素的模型的测试表明，使用本工作中建议的简单奖励功能，无人机以81％的目标率达到了目标。
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.
