2020 17th International Conference on Ubiquitous Robots (UR)最新文献_第3页

Robot Behavior Design Expressing Confidence/Unconfidence based on Human Behavior Analysis 基于人类行为分析的表达自信/不自信的机器人行为设计

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144862

Haruka Sekino, Erina Kasano, Wei-Fen Hsieh, E. Sato-Shimokawara, Toru Yamaguchi

{"title":"Robot Behavior Design Expressing Confidence/Unconfidence based on Human Behavior Analysis","authors":"Haruka Sekino, Erina Kasano, Wei-Fen Hsieh, E. Sato-Shimokawara, Toru Yamaguchi","doi":"10.1109/UR49135.2020.9144862","DOIUrl":"https://doi.org/10.1109/UR49135.2020.9144862","url":null,"abstract":"Dialogue robots have been actively researched. Many of these robots rely on merely using verbal information. However, human intention is conveyed using verbal information and nonverbal information. In order to convey intention as humans do, robots are necessary to express intention using verbal information and nonverbal information. This paper use speech information and head motion information to express confidence/unconfidence because they were useful features to estimate one’s confidence. First, human behavior expressing the presence or absence of confidence was collected from 8 participants. Human behavior was recorded by a microphone and a video camera. In order to select the behavior which is more understandable, the participants’ behavior was estimated for the confidence level by 3 estimators. Then the data of participants whose behavior was estimated to be more understandable were selected. The selected behavior was defined as representative speech feature and motion feature. Robot behavior was designed based on representative behavior. Finally, the experiment was conducted to evaluate the designed robot behavior. The robot behavior was estimated by 5 participants. The experiment results show that 3 participants estimated correctly the confidence/unconfidence behavior based on the representative speech feature. The differences between confidence and unconfidence of behavior are s the spent time before answer, the effective value of sound pressure, and utterance speed. Also, 3 participants estimated correctly the unconfidence behavior based on the representative motion features which are the longer spent time before answer and the bigger head rotation.","PeriodicalId":360208,"journal":{"name":"2020 17th International Conference on Ubiquitous Robots (UR)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

FPGA Implementation of Visual Noise Optimized Online Steady-State Motion Visual Evoked Potential BCI System* 视觉噪声优化在线稳态运动视觉诱发电位系统的FPGA实现*

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144933

Yanjun Zhang, Jun Xie, Guanghua Xu, Peng Fang, Guiling Cui, Guanglin Li, Guozhi Cao, Tao Xue, Xiaodong Zhang, Min Li, T. Tao

{"title":"FPGA Implementation of Visual Noise Optimized Online Steady-State Motion Visual Evoked Potential BCI System*","authors":"Yanjun Zhang, Jun Xie, Guanghua Xu, Peng Fang, Guiling Cui, Guanglin Li, Guozhi Cao, Tao Xue, Xiaodong Zhang, Min Li, T. Tao","doi":"10.1109/UR49135.2020.9144933","DOIUrl":"https://doi.org/10.1109/UR49135.2020.9144933","url":null,"abstract":"In order to improve the practicability of brain computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is necessary to design BCI equipment with portability and low-cost. According to the principle of stochastic resonance (SR), the recognition accuracy of visual evoked potential could be improved by full-screen visual noise. Based on the above requirements, this paper proposed the usage of field programmable gate array (FPGA) to control stimulator through high definition multimedia interface (HDMI) for the display of steady-state motion visual evoked potential (SSMVEP) paradigm. By adding spatially localized visual noise to the motion-reversal checkerboard paradigm, the recognition accuracy is improved. According to the experimental results under different noise levels, the average recognition accuracies calculated with occipital electrodes O1, Oz, O2, PO3, POz and PO4 are 77.2%, 87.5%, and 85.2% corresponding to noise standard deviations values of 0, 24, and 40, respectively. In order to analyze the SR effect on the recognition accuracy with utilization of spatially localized visual noise, statistical analyses on the recognition accuracies under different noise intensities and different channel combinations are carried out. Results showed that the spatially localized visual noise could significantly improve the recognition accuracy and the stability of the proposed FPGA based online SSMVEP BCI system.","PeriodicalId":360208,"journal":{"name":"2020 17th International Conference on Ubiquitous Robots (UR)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122430154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Human-robot negotiation of intentions based on virtual fixtures for shared task execution 基于虚拟夹具的人机意图协商共享任务执行

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144859

Dong Wei, Hua Zhou, Huayong Yang

引用次数: 1

Trajectory Tracking of Robotic Manipulators with Constraints Based on Model Predictive Control 基于模型预测控制的约束机器人轨迹跟踪

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144943

Q. Tang, Zhugang Chu, Yu Qiang, Shun Wu, Zheng Zhou

引用次数: 6

Recognition of Assembly Instructions Based on Geometric Feature and Text Recognition 基于几何特征和文本识别的装配指令识别

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144892

Jaewoo Park, Isaac Kang, Junhyeong Kwon, Eunji Lee, Yoonsik Kim, Sujeong You, S. Ji, N. Cho

{"title":"Recognition of Assembly Instructions Based on Geometric Feature and Text Recognition","authors":"Jaewoo Park, Isaac Kang, Junhyeong Kwon, Eunji Lee, Yoonsik Kim, Sujeong You, S. Ji, N. Cho","doi":"10.1109/UR49135.2020.9144892","DOIUrl":"https://doi.org/10.1109/UR49135.2020.9144892","url":null,"abstract":"Recent advances in machine learning methods have increased the performances of object detection and recognition systems. Accordingly, automatic understanding of assembly instructions in manuals in the form of electronic or paper materials has also become an issue in the research community. This task is quite challenging because it requires the automatic optical character recognition (OCR) and also the understanding of various mechanical parts and diverse assembly illustrations that are sometimes difficult to understand even for humans. Although deep networks are showing high performance in many computer vision tasks, it is still difficult to perform this task by an end-to-end deep neural network due to the lack of training data, and also because of diversity and ambiguity of illustrative instructions. Hence, in this paper, we propose to tackle this problem by using both conventional non-learning approaches and deep neural networks, considering the current state-of-the-arts. Precisely, we first extract components having strict geometric structures, such as characters and illustrations, by conventional non-learning algorithms, and then apply deep neural networks to recognize the extracted components. The main targets considered in this paper are the types and the numbers of connectors, and behavioral indicators such as circles, rectangles, and arrows for each cut in do-it-yourself (DIY) furniture assembly manuals. For these limited targets, we train a deep neural network to recognize them with high precision. Experiments show that our method works robustly in various types of furniture assembly instructions.","PeriodicalId":360208,"journal":{"name":"2020 17th International Conference on Ubiquitous Robots (UR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125125944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Implementation of a unified simulation for robot arm control with object detection based on ROS and Gazebo 基于ROS和Gazebo的机器人手臂控制与目标检测的统一仿真实现

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144984

Hyeonchul Jung, Min-Soo Kim, Yeheng Chen, H. Min, Taejoon Park

引用次数: 2

A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm 基于纳什遗传算法的仿生翼型优化技术

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144868

Hamid Isakhani, C. Xiong, Shigang Yue, Wenbin Chen

引用次数: 3

The Design and Implementation of Human Motion Capture System Based on CAN Bus * 基于CAN总线的人体运动捕捉系统的设计与实现

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144858

Xian Yue, Aibin Zhu, Jiyuan Song, Guangzhong Cao, Delin An, Zhifu Guo

引用次数: 2

Design and Control of a Piezoelectric Actuated Prostate Intervention Robotic System* 压电驱动前列腺介入机器人系统的设计与控制

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144768

Yuyang Lin, Yunlai Shi, Jun Zhang, Fugang Wang, Wenbo Wu, Haichao Sun

{"title":"Design and Control of a Piezoelectric Actuated Prostate Intervention Robotic System*","authors":"Yuyang Lin, Yunlai Shi, Jun Zhang, Fugang Wang, Wenbo Wu, Haichao Sun","doi":"10.1109/UR49135.2020.9144768","DOIUrl":"https://doi.org/10.1109/UR49135.2020.9144768","url":null,"abstract":"Robot-assisted prostate intervention under Magnetic resonance imaging (MRI) guidance is a promising method to improve the clinical performance compared with the manual method. An MRI-guided 6-DOF prostate intervention serial robot fully actuated by ultrasonic motors is designed and the control strategy is proposed. The mechanical design of the proposed robot is presented based on the design requirements of the prostate intervention robot. The binocular vision is adopted as the in-vitro needle tip measurement method and the robotic system combined with the binocular cameras are illustrated. Then the ultrasonic motor driving controller is designed. Finally, the position accuracy evaluation of the robot is carried out and the position error is about 1.898 mm which shows a good accuracy of the robot. The position tracking characteristics of the ultrasonic motor is presented where the maximum tracking error is under 7.5° which shows the efficiency of the driving controller design. The experiments indicate that the prostate intervention robot is feasible and shows good performance in the accuracy evaluation.","PeriodicalId":360208,"journal":{"name":"2020 17th International Conference on Ubiquitous Robots (UR)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

sEMG-based Static Force Estimation for Human-Robot Interaction using Deep Learning 基于表面肌电信号的深度学习人机交互静态力估计

2020 17th International Conference on Ubiquitous Robots (UR) Pub Date : 2020-06-01 DOI: 10.1109/UR49135.2020.9144869

Se Jin Kim, W. Chung, Keehoon Kim

{"title":"sEMG-based Static Force Estimation for Human-Robot Interaction using Deep Learning","authors":"Se Jin Kim, W. Chung, Keehoon Kim","doi":"10.1109/UR49135.2020.9144869","DOIUrl":"https://doi.org/10.1109/UR49135.2020.9144869","url":null,"abstract":"Human-robot interaction (HRI) is a rapidly growing research area and it occurs in many applications including human-robot collaboration, human power augmentation, and rehabilitation robotics. As it is hard to exactly calculate intended motion trajectory, generally, interaction control is applied in HRI instead of pure motion control. To implement the interaction control, force information is necessary and force sensor is widely used in force feedback. However, force sensor has some limitations as 1) it is subject to breakdown, 2) it imposes additional volume and weight to the system, and 3) its applicable places are constrained. In this situation, force estimation can be a good solution. However, if force in static situation should be measured, using position and velocity is not sufficient because they are not influenced by the exerted force anymore. Therefore, we proposed sEMG-based static force estimation using deep learning. sEMG provides a useful information about human-exerting force because it reflects the human intention. Also, to extract the complex relationship between sEMG and force, deep learning approach is used. Experimental results show that when force with maximal value of 63.2 N is exerted, average force estimation error was 3.67 N. Also, the proposed method shows that force onset timing of estimated force is faster than force sensor signal. This result would be advantageous for faster human intention recognition.","PeriodicalId":360208,"journal":{"name":"2020 17th International Conference on Ubiquitous Robots (UR)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132458513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1