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An innovative bio-inspired flight controller for quad-rotor drones: Quad-rotor drone learning to fly using reinforcement learning

Robotics and autonomous systems, v.135, 2021년, pp.103671 -   

Ramezani Dooraki, Amir (Corresponding authors.) ,  Lee, Deok-Jin

Abstract AI-Helper 아이콘AI-Helper

Abstract Animals learn to master their capabilities by trial and error, and with out having any knowledge about their dynamics model and mathematical or physical rules. They use their maximum capabilities in an optimized way. This is the result of millions of years of evolution where the best of di...

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참고문헌 (30)

  1. Floreano 2008 Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies 

  2. Sutton 1998 Introduction to reinforcement learning 

  3. Dooraki 2018 Experience, imitation and reflection; confucius’ conjecture and machine learning 

  4. Nature Mnih 518 7540 529 2015 10.1038/nature14236 Human-level control through deep reinforcement learning 

  5. Nature Silver 529 7587 484 2016 10.1038/nature16961 Mastering the game of go with deep neural networks and tree search 

  6. Silver 2017 Mastering chess and shogi by self-play with a general reinforcement learning algorithm 

  7. stockfishchess.org, Stockfish Chess Engine, URL: http://stockfishchess.org/. 

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  9. Forestier 2016 The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS 2016) Autonomous exploration, active learning and human guidance with open-source poppy humanoid robot platform and explauto library 

  10. Artificial Intelligence Kompella 247 Supplement C 313 2017 10.1016/j.artint.2015.02.001 Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots 

  11. Sensors Ramezani Dooraki 18 10 2018 An end-to-end deep reinforcement learning-based intelligent agent capable of autonomous exploration in unknown environments 

  12. Lillicrap 2015 Continuous control with deep reinforcement learning 

  13. Schulman 2015 Trust region policy optimization 

  14. Schulman 2017 Proximal policy optimization algorithms 

  15. Dooraki 118 2019 2019 16th International Conference on Ubiquitous Robots (UR) Multi-rotor robot learning to fly in a bio-inspired way using reinforcement learning 

  16. Sadeghi 2016 (Cad)$?2$rl: Real single-image flight without a single real image 

  17. Zhang 2015 Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search 

  18. Ng 363 2006 Experimental Robotics IX Autonomous inverted helicopter flight via reinforcement learning 

  19. Abbeel 1 2006 Proceedings of the 19th International Conference on Neural Information Processing Systems An application of reinforcement learning to aerobatic helicopter flight 

  20. Gazebosim.org, Gazebo Simulator, URL: http://gazebosim.org/. 

  21. Furrer 595 2016 Robot Operating System (ROS): The Complete Reference (Volume 1) 

  22. IEEE Robot. Autom. Lett. Hwangbo 2 4 2096 2017 10.1109/LRA.2017.2720851 Control of a quadrotor with reinforcement learning 

  23. Lambert 2019 Low level control of a quadrotor with deep model-based reinforcement learning 

  24. ACM Trans. Cyber-Phys. Syst. Koch 3 2 22:1 2019 10.1145/3301273 Reinforcement learning for UAV attitude control 

  25. Kakade 267 2002 Proceedings of the Nineteenth International Conference on Machine Learning Approximately optimal approximate reinforcement learning 

  26. Mnih 1928 2016 Proceedings of the 33rd International Conference on Machine Learning Asynchronous methods for deep reinforcement learning 

  27. Bengio 41 2009 Proceedings of the 26th Annual International Conference on Machine Learning Curriculum learning 

  28. R.O.S.org, ROS.org - Powering the world’s robots, URL: http://www.ros.org/. 

  29. Dhariwal 2017 Openai baselines 

  30. M. Kamel, T. Stastny, K. Alexis, R. Siegwart, Model predictive control for trajectory tracking of unmanned aerial vehicles using robot operating system, in: Koubaa, A. (Ed.), Robot Operating System (ROS) the Complete Reference, Volume 2, Springer. 

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