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A deep reinforcement learning approach for path following on a quadrotor

Author
Rubi, B.; Morcego, B.; Perez, R.
Type of activity
Presentation of work at congresses
Name of edition
2020 European Control Conference
Date of publication
2020
Presentation's date
2020-05-14
Book of congress proceedings
Proceedings of the 2020 European Control Conference (ECC): Saint Petersburg, Russia, May 12-15, 2020
First page
1092
Last page
1098
Project funding
Intelligent Supervision for the Predictive Maintenance of Industrial Systems
Safety and control of autonomous vehicles
Repository
http://hdl.handle.net/2117/328906 Open in new window
https://www.researchgate.net/publication/341384886_A_Deep_Reinforcement_Learning_Approach_for_Path_Following_on_a_Quadrotor Open in new window
URL
https://ieeexplore.ieee.org/document/9143591 Open in new window
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 g...
Citation
Rubi, 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.
Keywords
Attitude control, Heuristic algorithms, Learning (artificial intelligence), Machine learning, Prediction algorithms, Training, Unmanned aerial vehicles
Group of research
CS2AC-UPC - Supervision, Safety and Automatic Control

Participants

Attachments