A Deep Reinforcement Learning Approach for Path Following on a Quadrotor
Bartomeu Rubí, Bernardo Morcego, Ramón Pérez
Abstract
This paper proposes the Deep Deterministic Policy Grandient (DDPG) reinforcement learning algorithm to solve the path following problem in a quadrotor vehicle. This agent is implemented using a separated control and guidance structure with an autopilot tracking the attitude and velocity commands. The DDPG agent is implemented in python and it is trained and tested in the RotorS-Gazebo environment, a realistic multirotor simulator integrated in ROS. Performance is compared with Adaptive NLGL, a geometric algorithm that implements an equivalent control structure. Results show how the DDPG agent is able to outperform the Adaptive NLGL approach while reducing its complexity.
Topics & Concepts
MultirotorReinforcement learningAutopilotPython (programming language)Computer sciencePath (computing)Control theory (sociology)Control engineeringTracking (education)Attitude controlArtificial intelligenceSimulationControl (management)EngineeringAerospace engineeringPedagogyOperating systemProgramming languagePsychologyRobotic Path Planning AlgorithmsGuidance and Control SystemsReinforcement Learning in Robotics