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人类行为模式启发的人机物体传递
引用本文:王宪伟,丛明,刘冬,杜宇,邹强. 人类行为模式启发的人机物体传递[J]. 组合机床与自动化加工技术, 2021, 0(2): 1-5
作者姓名:王宪伟  丛明  刘冬  杜宇  邹强
作者单位:大连理工大学机械工程学院;大连大华中天科技有限公司
基金项目:国家自然科学基金资助项目(61873045);大连市科技创新基金资助项目(2018J12GX035)。
摘    要:针对目前人到机器人的物体传递过程中,机器人难以像人类一样理解给予者的意图并配合完成传递任务的问题,提出了一种人类行为模式启发的人机物体传递方法.通过观察人类物体传递过程总结了人类的物体传递行为模式,使用骨骼信息与RGB-D信息融合构建传递意图判别模型;控制机器人在识别到传递意图后迅速配合完成传递任务;通过相关实验验证了...

关 键 词:人机物体传递  意图识别  人类行为模式  人机交互

A Human-to-robot Handover Method Inspired by Human Behavior Patterns
WANG Xian-wei,CONG Ming,LIU Dong,DU Yu,ZOU Qiang. A Human-to-robot Handover Method Inspired by Human Behavior Patterns[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021, 0(2): 1-5
Authors:WANG Xian-wei  CONG Ming  LIU Dong  DU Yu  ZOU Qiang
Affiliation:(School of Mechanical Engineering, Dalian University of Technology,Dalian Liaoning 116024,China;Dalian Dahuazhongtian Technology Co., Ltd., Dalian Liaoning 116025,China)
Abstract:In view of the current robot′s inability to understand the giver′s potential intention as well as the human being in the handover process,this paper proposed a human-to-robot handover method inspired by human behavior patterns.This paper first summarized the human handover behavior pattern by observing the relevant process,and constructed the delivery intention discrimination model by using the skeletion information and RGB-D information.Then the robot was controlled to complete the handover task after recognizing the delivery intention.Finally,the improvement of human-to-robot handover efficiency was verified by relevant experiments,and a questionnaire survey based on the Robot Social Attribute Scale(RoSAS)was conducted among the users.The experiment shows that the accuracy of the delivery intent recognition proposed in this paper is 96.2%,and it effectively distinguishes different intentions expressed in the same pose.The success rate of the human-to-robot handover system is 91.1%,and the average handover time is 3.3 s,which can meet the needs of accuracy and fluency in human-to-robot handover.
Keywords:human-to-robot handover  intention recognition  human behavior patterns  human-robot interaction
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