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Tiltrotors Position Tracking Controller Design Using Deep Reinforcement Learning
Huo YJ(霍雨佳)1,2,3; Li YP(李一平)2,3; Feng XS(封锡盛)2,3
Department水下机器人研究室
Conference Name2019 5th International Conference on Mechanical and Aeronautical Engineering (ICMAE 2019)
Conference DateDecember 12-15, 2019
Conference PlaceSanya, China
Source Publication2019 5th International Conference on Mechanical and Aeronautical Engineering (ICMAE 2019)
PublisherIOP
Publication PlaceBristol, UK
2019
Pages1-9
Contribution Rank1
ISSN1757-8981
Keyworddeep reinforcement learning tiltrotors robot trans-domain robot position tracking
AbstractIn this paper, a quad-tiltrotors air-water trans-domain robot is introduced. The nonlinear dynamic behaviours with uncertainties require a robust controller for multi-tasks. For this robot, controllers are designed using deep reinforcement learning method solving position and attitude control when operating as a UAV in the air. A ROS combining Gazebo simulation platform is designed to train the robot. The simulation results show the tiltrotors robot gets capabilities of spots tracking as a quad-rotors, and trajectory tracking as both the quad-rotors and tiltrotors.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26534
Collection水下机器人研究室
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing, 100049, China
Recommended Citation
GB/T 7714
Huo YJ,Li YP,Feng XS. Tiltrotors Position Tracking Controller Design Using Deep Reinforcement Learning[C]. Bristol, UK:IOP,2019:1-9.
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