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Memory-Based Reinforcement Learning for Trans-Domain Tiltrotor Robot Control
Huo YJ(霍雨佳)1,2,3; Li YP(李一平)1,2; Feng XS(封锡盛)1,2
Department水下机器人研究室
Conference Name2019 10th Asia Conference on Mechanical and Aerospace Engineering, ACMAE 2019
Conference DateDecember 26-28, 2019
Conference PlaceBangkok, Thailand
Source Publication2019 10th Asia Conference on Mechanical and Aerospace Engineering
PublisherIOP
Publication PlaceBristol, UK
2019
Pages1-9
Indexed ByEI ; CPCI(ISTP)
EI Accession number20202208760067
WOS IDWOS:000583802000011
Contribution Rank1
ISSN1742-6588
AbstractAiming at the problems of motion control with high precision for a new type of air-water trans-domain tiltrotors, a deep reinforcement learning controller is applied to these conditions. Reinforcement learning algorithm with memory capability allows the robot to learn from dynamic information collected in the past. In this paper, the trans-domain tiltrotors are supposed operating as a quad-rotors with fixed-wing in the air. Moreover, simulation is based on ROS and Gazebo platform for training the reinforcement learning repeatedly and the results demonstrate this algorithm gets better accuracy and effectiveness compared with other non-current methods in the conditions of the tiltrotors control task.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26938
Collection水下机器人研究室
Corresponding AuthorHuo YJ(霍雨佳)
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. Memory-Based Reinforcement Learning for Trans-Domain Tiltrotor Robot Control[C]. Bristol, UK:IOP,2019:1-9.
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