  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.

Robotics;Machine Learning