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.

该研究旨在通过应用非线性深度强化学习（DRL）代理作为传统线性比例积分微分（PID）控制器的替代品，从而彻底改革无人机飞行控制。主要目标是实现无人机在手动和自主模式之间的无缝过渡，提高其响应性和稳定性。利用 Gazebo 模拟器中的 Proximal Policy Optimization（PPO）强化学习策略训练 DRL 代理，并通过添加价格为 20000 美元的室内 Vicon 跟踪系统实现 < 1mm 的定位精度，从而大大提高了自主飞行的精确性。为了在最短的无碰撞轨迹下引导无人机导航，我们还构建了一个三维 A * 路径规划器，并成功将其应用到实际飞行中。