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A PPO-based DRL Auto-Tuning Nonlinear PID Drone Controller for Robust Autonomous Flights
April 2, 2024, 7:43 p.m. | Junyang Zhang, Cristian Emanuel Ocampo Rivera, Kyle Tyni, Steven Nguyen
cs.LG updates on arXiv.org arxiv.org
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, …
abstract agent arxiv auto autonomous control cs.lg cs.ro cs.sy drone drones eess.sy flights integral linear ppo project reinforcement reinforcement learning replacement robust transition type
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