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Hierarchical Human Motion Intention Prediction for Increasing Efficacy of Human-Robot Collaboration

文献类型：期刊论文

作者Meng, Lingyi1,2; Yang, Lin3; Zheng, Enhao1
刊名IEEE ROBOTICS AND AUTOMATION LETTERS
出版日期2024-09-01
卷号9期号:9页码:7637-7644
关键词Task analysis Robots Trajectory Muscles Predictive models Collaboration Sawing Human-robot collaboration human motion intent human motion prediction human motion decoding efficacy
ISSN号2377-3766
DOI10.1109/LRA.2024.3430131
通讯作者Zheng, Enhao(enhao.zheng@ia.ac.cn)
英文摘要When humans and robots work together to accomplish tasks with dynamic uncertainty, the robots should perceive human motion intentions so as to cooperate with humans and increase efficacy. In this study, we propose a hierarchical motion intention prediction model for human-robot collaboration, in which the bottom level acquires human motion information, the middle level recognizes motion states and the high level predicts motion intentions. Compared with existing methods, our model fuses task-level human behavioral pattern prediction with instantaneous continuous motion intent decoding. Therefore, the robot controller can generate a collaborative trajectory in advance and adjust the key parameters (forces and velocities, etc.) in real time according to human motions. We quantitatively verify the proposed model with 10 subjects in the human-robot sawing task. The results show that the hierarchical model can effectively reduce human energy consumption and improve the average speed of the task. Meanwhile, subjective metrics indicate that subjects believe robots employing hierarchical models as capable of fostering improved cooperation and delivering greater assistance. Our study systematically proves that the proposed hierarchical model significantly enhanced the efficiency of human-robot co-manipulation, marking a step forward compared with existing works. Future studies will be focused on investigating more complex and general tasks.
WOS关键词MANIPULATION ; STRATEGY
资助项目National Natural Science Foundation of China[62073318] ; Youth Innovation Promotion Association CAS
WOS研究方向Robotics
语种英语
WOS记录号WOS:001276400400015
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/59382]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Zheng, Enhao
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore
推荐引用方式
GB/T 7714		 Meng, Lingyi,Yang, Lin,Zheng, Enhao. Hierarchical Human Motion Intention Prediction for Increasing Efficacy of Human-Robot Collaboration[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2024,9(9):7637-7644.
APA Meng, Lingyi,Yang, Lin,&Zheng, Enhao.(2024).Hierarchical Human Motion Intention Prediction for Increasing Efficacy of Human-Robot Collaboration.IEEE ROBOTICS AND AUTOMATION LETTERS,9(9),7637-7644.
MLA Meng, Lingyi,et al."Hierarchical Human Motion Intention Prediction for Increasing Efficacy of Human-Robot Collaboration".IEEE ROBOTICS AND AUTOMATION LETTERS 9.9(2024):7637-7644.

入库方式： OAI收割

来源：自动化研究所

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