ROBOMECH Journal最新文献_第9页

Dual edge classifier for robust cloth unfolding 双边缘分类器，坚固的布展开

IF 1.4

ROBOMECH Journal Pub Date : 2021-01-25 DOI: 10.1186/s40648-021-00202-8

Antonio Gabas, Y. Kita, E. Yoshida

引用次数: 4

Evaluation of the deformation shape of a balloon-type dielectric elastomer actuator prestretched with water pressure 水压预拉伸球囊式介电弹性体作动器的变形形状评估

IF 1.4

ROBOMECH Journal Pub Date : 2021-01-18 DOI: 10.1186/s40648-021-00189-2

Natsumi Koike, T. Hayakawa

引用次数: 0

Design and implementation of a maxi-sized mobile robot (Karo) for rescue missions 用于救援任务的大型移动机器人(Karo)的设计与实现

IF 1.4

ROBOMECH Journal Pub Date : 2021-01-07 DOI: 10.1186/s40648-020-00188-9

Soheil Habibian, Mehdi Dadvar, Behzad Peykari, Alireza H. M. Hosseini, M. Salehzadeh, Alireza H. M. Hosseini, Farshid Najafi

引用次数: 35

Dynamic manipulation of unknown string by robot arm: realizing momentary string shapes 机械臂对未知弦的动态操纵:实现瞬时弦形

IF 1.4

ROBOMECH Journal Pub Date : 2020-12-01 DOI: 10.1186/s40648-020-00187-w

K. Tabata, H. Seki, Tokuo Tsuji, Tatsuhiro Hiramitsu, M. Hikizu

引用次数: 4

A robotic end-effector with rolling up mechanism for pick-and-release of a cotton sheet 一种用于棉片拾取和释放的具有卷起机构的机器人末端执行器

IF 1.4

ROBOMECH Journal Pub Date : 2020-10-20 DOI: 10.1186/s40648-020-00186-x

Taiki Abe, Y. Kawasaki, Kimitoshi Yamazaki

引用次数: 2

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output 基于传感器输出的噪声协方差矩阵的扩展卡尔曼滤波器姿态估计

IF 1.4

ROBOMECH Journal Pub Date : 2020-10-19 DOI: 10.1186/s40648-020-00185-y

Ayuko Saito, Satoru Kizawa, Yoshikazu Kobayashi, K. Miyawaki

引用次数: 7

Locomotion approach of bipedal robot utilizing passive wheel without swing leg based on stability margin maximization and fall prevention functions 基于稳定裕度最大化和防摔功能的无摆腿被动轮式两足机器人运动方法

IF 1.4

ROBOMECH Journal Pub Date : 2020-10-15 DOI: 10.1186/s40648-020-00182-1

Kohei Kimura, Noriaki Imaoka, Shintaro Noda, Yohei Kakiuchi, K. Okada, M. Inaba

引用次数: 0

Study of a robotic system to detect water leakage and fuel debris-System proposal and feasibility study of visual odometry providing intuitive bird’s eye view- 一种检测漏水和燃料碎片的机器人系统的研究-提供直观鸟瞰的视觉里程计系统方案和可行性研究-

IF 1.4

ROBOMECH Journal Pub Date : 2020-10-13 DOI: 10.1186/s40648-020-00184-z

Zhenyu Wang, G. Endo, Masashi Takahashi, Hiroyuki Nabae, K. Suzumori, N. Tsuzuki, H. Takahashi, K. Kimoto, T. Ihara, H. Kikura

引用次数: 0

Projection-mapping-based object pointing using a high-frame-rate camera-projector system 基于投影映射的物体指向，使用高帧率的摄像机-投影仪系统

IF 1.4

ROBOMECH Journal Pub Date : 2020-09-18 DOI: 10.21203/rs.3.rs-78087/v1

Deepak Kumar, Sushil Raut, Kohei Shimasaki, T. Senoo, I. Ishii

{"title":"Projection-mapping-based object pointing using a high-frame-rate camera-projector system","authors":"Deepak Kumar, Sushil Raut, Kohei Shimasaki, T. Senoo, I. Ishii","doi":"10.21203/rs.3.rs-78087/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-78087/v1","url":null,"abstract":"The novel approach to physical security based on visible light communication (VLC) using an informative object-pointing and simultaneous recognition by high-framerate (HFR) vision systems is presented in this study. In the proposed approach, a convolutional neural network (CNN) based object detection method is used to detect the environmental objects that assist a spatiotemporal-modulated-pattern (SMP) based imperceptible projection mapping for pointing the desired objects. The distantly located HFR vision systems that operate at hundreds of frames per second (fps) can recognize and localize the pointed objects in real-time. The prototype of an artificial intelligence-enabled camera-projector (AiCP) system is used as a transmitter that detects the multiple objects in real-time at 30 fps and simultaneously projects the detection results by means of the encoded-480-Hz-SMP masks on to the objects. The multiple 480-fps HFR vision systems as receivers can recognize the pointed objects by decoding pixel-brightness variations in HFR sequences without any camera calibration or complex recognition methods. Several experiments were conducted to demonstrate our proposed method’s usefulness using miniature and real-world objects under various conditions.","PeriodicalId":37462,"journal":{"name":"ROBOMECH Journal","volume":"8 1","pages":"1-21"},"PeriodicalIF":1.4,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43281525","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

Recalling of multiple grasping methods from an object image with a convolutional neural network 基于卷积神经网络的物体图像抓取方法回顾

IF 1.4

ROBOMECH Journal Pub Date : 2020-08-13 DOI: 10.21203/rs.3.rs-51700/v1

M. Sanada, T. Matsuo, N. Shimada, Y. Shirai

{"title":"Recalling of multiple grasping methods from an object image with a convolutional neural network","authors":"M. Sanada, T. Matsuo, N. Shimada, Y. Shirai","doi":"10.21203/rs.3.rs-51700/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-51700/v1","url":null,"abstract":"In this study, a method for a robot to recall multiple grasping methods for a given object is proposed. The aim of this study was for robots to learn grasping methods for new objects by observing the grasping activities of humans in daily life without special instructions. For this setting, only one grasping motion was observed for an object at a time, and it was never known whether other grasping methods were possible for the object, although supervised learning generally requires all possible answers for each training input. The proposed method gives a solution for that learning situations by employing a convolutional neural network with automatic clustering of the observed grasping method. In the proposed method, the grasping methods are clustered during the process of learning of the grasping position. The method first recalls grasping positions and the network estimates the multi-channel heatmap such that each channel heatmap indicates one grasping position, then checks the graspability for each estimated position. Finally, the method recalls the hand shapes based on the estimated grasping position and the object’s shape. This paper describes the results of recalling multiple grasping methods and demonstrates the effectiveness of the proposed method.","PeriodicalId":37462,"journal":{"name":"ROBOMECH Journal","volume":"8 1","pages":"1-13"},"PeriodicalIF":1.4,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43101378","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: 0