Abstract
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.
Abstract (translated)
本项目旨在通过实现一个非线性深度强化学习（DRL）代理来颠覆无人机飞行控制，用来说明传统的线性比例微分（PID）控制器。主要目标是使无人机无缝地在手动和自动驾驶模式之间转换，提高反应性和稳定性。我们在Gazebo仿真器中利用Proximal Policy Optimization（PPO）强化学习策略来训练DRL代理。增加一个20,000美元的室内Vicon跟踪系统提供了<1mm的定位精度，这显著提高了自主飞行的精确度。为了在最近的碰撞避免轨迹中引导无人机，我们还构建了一个3维A*路径规划器，并成功地将其融入到实际飞行中。
URL
https://arxiv.org/abs/2404.00204
