A deep reinforcement learning approach for path following on a quadrotor
Document typeConference report
Rights accessOpen Access
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.
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CitationRubi, B.; Morcego, B.; Perez, R. A deep reinforcement learning approach for path following on a quadrotor. A: European Control Conference. "Proceedings of the 2020 European Control Conference (ECC): Saint Petersburg, Russia, May 12-15, 2020". 2020, p. 1092-1098. ISBN 978-3-907144-02-2.
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