A PPO-based DRL Auto-Tuning Nonlinear PID Drone Controller for Robust
  Autonomous Flights
Junyang Zhang Cristian Emanuel Ocampo Rivera Kyle Tyni Steven Nguyen
This project aims to revolutionize drone flight control by implementing a
nonlinear Deep Reinforcement Learning (DRL) agent as a replacement for
traditional linear Proportional Integral Derivative (PID) controllers. The
primary objective is to seamlessly transition drones between manual and
autonomous modes, enhancing responsiveness and stability. We utilize the
Proximal Policy Optimization (PPO) reinforcement learning strategy within the
Gazebo simulator to train the DRL agent. Adding a $20,000 indoor Vicon tracking
system offers <1mm positioning accuracy, which significantly improves
autonomous flight precision. To navigate the drone in the shortest
collision-free trajectory, we also build a 3 dimensional A* path planner and
implement it into the real flight successfully.