机器人手臂的感知系统,将物体传递给车内乘客

Li Jun, Tee Keng Peng, C. Lawrence, Wan Kong Wah, Yau Wei Yun
{"title":"机器人手臂的感知系统,将物体传递给车内乘客","authors":"Li Jun, Tee Keng Peng, C. Lawrence, Wan Kong Wah, Yau Wei Yun","doi":"10.1109/APSIPA.2017.8282065","DOIUrl":null,"url":null,"abstract":"Automatically delivering objects to in-car passengers has many potential applications. Such a system generally consists of two sub-systems: a perception system and an action system. The perception system basically looks for the targets' positions and the action system delivers objects to the targets. In this paper, we propose a novel perception system, which contains two major functions: estimation of reaching points and discovering potential risks. The reaching points are the locations where robot arms needs to reach. Moreover, it should be able to reach with comfort by passengers and keep a safe distance from the car body. In order to achieve this, all the vehicle components (side surfaces, side mirrors etc.), which may cause collision, need to be detected. Potential risks are usually caused by moving objects or changing door state (close to open) during the operation. It is necessary to monitor these two situations to avoid any potential risks during operation. Our offline test shows that the accuracy of reaching points estimation can reach up to 94% and the response time for moving objects detection or door state changes is less than 1 millisecond.","PeriodicalId":142091,"journal":{"name":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A perception system for robot arms to convey objects to in-car passengers\",\"authors\":\"Li Jun, Tee Keng Peng, C. Lawrence, Wan Kong Wah, Yau Wei Yun\",\"doi\":\"10.1109/APSIPA.2017.8282065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatically delivering objects to in-car passengers has many potential applications. Such a system generally consists of two sub-systems: a perception system and an action system. The perception system basically looks for the targets' positions and the action system delivers objects to the targets. In this paper, we propose a novel perception system, which contains two major functions: estimation of reaching points and discovering potential risks. The reaching points are the locations where robot arms needs to reach. Moreover, it should be able to reach with comfort by passengers and keep a safe distance from the car body. In order to achieve this, all the vehicle components (side surfaces, side mirrors etc.), which may cause collision, need to be detected. Potential risks are usually caused by moving objects or changing door state (close to open) during the operation. It is necessary to monitor these two situations to avoid any potential risks during operation. Our offline test shows that the accuracy of reaching points estimation can reach up to 94% and the response time for moving objects detection or door state changes is less than 1 millisecond.\",\"PeriodicalId\":142091,\"journal\":{\"name\":\"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APSIPA.2017.8282065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSIPA.2017.8282065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

自动向车内乘客递送物品有许多潜在的应用。这种系统一般由两个子系统组成:感知系统和行动系统。感知系统主要是寻找目标的位置,而行动系统则将物体传递给目标。在本文中,我们提出了一个新的感知系统,它包含两个主要功能:到达点的估计和潜在风险的发现。到达点是机器人手臂需要到达的位置。此外,它应该能够让乘客舒适地接触到,并与车身保持安全距离。为了实现这一目标,需要检测可能导致碰撞的所有车辆部件(侧表面,侧后视镜等)。潜在的危险通常是由于在操作过程中移动物体或改变门的状态(关闭或打开)而引起的。有必要对这两种情况进行监测,以避免在操作过程中出现任何潜在的风险。我们的离线测试表明,到达点估计的准确率可以达到94%,移动物体检测或门状态变化的响应时间小于1毫秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A perception system for robot arms to convey objects to in-car passengers
Automatically delivering objects to in-car passengers has many potential applications. Such a system generally consists of two sub-systems: a perception system and an action system. The perception system basically looks for the targets' positions and the action system delivers objects to the targets. In this paper, we propose a novel perception system, which contains two major functions: estimation of reaching points and discovering potential risks. The reaching points are the locations where robot arms needs to reach. Moreover, it should be able to reach with comfort by passengers and keep a safe distance from the car body. In order to achieve this, all the vehicle components (side surfaces, side mirrors etc.), which may cause collision, need to be detected. Potential risks are usually caused by moving objects or changing door state (close to open) during the operation. It is necessary to monitor these two situations to avoid any potential risks during operation. Our offline test shows that the accuracy of reaching points estimation can reach up to 94% and the response time for moving objects detection or door state changes is less than 1 millisecond.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Locomotion control of a serpentine crawling robot inspired by central pattern generators On the construction of more human-like chatbots: Affect and emotion analysis of movie dialogue data Pose-invariant kinematic features for action recognition CNN-based bottleneck feature for noise robust query-by-example spoken term detection Robust template matching using scale-adaptive deep convolutional features
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1