IoT-Enabled Dual-Arm Motion Capture and Mapping for Telerobotics in Home Care

2020 ◽  
Vol 24 (6) ◽  
pp. 1541-1549 ◽  
Author(s):  
Huiying Zhou ◽  
Geng Yang ◽  
Honghao Lv ◽  
Xiaoyan Huang ◽  
Huayong Yang ◽  
...  
Keyword(s):  
Home Care ◽  
Motion Capture ◽  
Arm Motion ◽  
Dual Arm

scholarly journals Pouring from Deformable Containers Based on Tactile Information Using a Dual-Arm Robot

2019 ◽  
Vol 1 (2) ◽  
pp. 123-143
Author(s):  
Yuto Tsuchiya
Keyword(s):  
Success Rate ◽  
Motion Capture ◽  
The Other ◽  
Motion Capture System ◽  
Breakfast Cereal ◽  
Tactile Information ◽  
Coffee Beans ◽  
Arm Motion ◽  
Dual Arm Robot ◽  
Dual Arm

In this paper, we consider household robots that pour various contents from deformable containers. Such pouring is often seen in cooking and refilling. To achieve this kind of pouring, we reduce the deformation of the container during pouring and thus carefully design the grasping strategy: the palm of one hand supports the deformable container from the bottom and the other hand pulls up the container from the top. We apply the proposed system to pouring four different kinds of contents: breakfast cereal, coffee beans, flour, and rice. The experiment verifies that the proposed system successfully pours the four contents. To evaluate the system quantitatively, we measure 1) the deformation of the container using a motion capture system and 2) the success rate of pouring. We verify that the dual-arm pouring reduced the deformation by 66% compared to a single-arm motion and that the success rate is greater than 90%.

Investigation of Synchronous Accuracy of Dual Arm Motion of Industrial Robot

2012 ◽  
Vol 516 ◽  
pp. 234-239 ◽  
Author(s):  
Wei Wu ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama
Keyword(s):  
Rotational Motion ◽  
Manufacturing Systems ◽  
Industrial Robot ◽  
Circular Path ◽  
Stable Operation ◽  
Motion Error ◽  
Ball Rolling ◽  
Arm Motion ◽  
Five Axis ◽  
Dual Arm

Recently, new needs have emerged to control not only linear motion but also rotational motion in high-accuracy manufacturing fields. Many five-axis-controlled machining centres are therefore in use. However, one problem has been the difficulty of creating flexible manufacturing systems with methods based on the use of these machine tools. On the other hand, the industrial dual-arm robot has gained attention as a new way to achieve accurate linear and rotational motion in an attempt to control a working plate like a machine tool table. In the present report, a cooperating dual-arm motion is demonstrated to make it feasible to perform stable operation control, such as controlling the working plate to keep a ball rolling around a circular path on it. As a result, we investigated the influence of each axis motion error on a ball-rolling path.

scholarly journals Simultaneous Dual-Arm Motion Planning for Minimizing Operation Time

2017 ◽  
Vol 7 (12) ◽  
pp. 1210 ◽  
Author(s):  
Jun Kurosu ◽  
Ayanori Yorozu ◽  
Masaki Takahashi
Keyword(s):  
Motion Planning ◽  
Operation Time ◽  
Arm Motion ◽  
Dual Arm

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Motion Capture ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Human Motion ◽  
Home Based ◽  
Human Arm ◽  
Arm Motion

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

Discrimination of Dual-Arm Motions Using a Joint Posterior Probability Neural Network for Human-Robot Interfaces

2018 ◽  
pp. 347-374
Author(s):  
Taro Shibanoki ◽  
Toshio Tsuji
Keyword(s):  
Posterior Probability ◽  
Discrimination Performance ◽  
Robotic Manipulator ◽  
Posterior Probabilities ◽  
Arm Motions ◽  
High Discrimination ◽  
Left And Right ◽  
Arm Motion ◽  
Human Robot Interfaces ◽  
Dual Arm

This chapter describes a novel dual-arm motion discrimination method that combines posterior probabilities estimated independently for left and right arm movements, and its application to control a robotic manipulator. The proposed method estimates the posterior probability of each single-arm motion through learning using recurrent probabilistic neural networks. The posterior probabilities output from the networks are then combined based on motion dependency between arms, making it possible to calculate a joint posterior probability of dual-arm motions. With this method, all the dual-arm motions consisting of each single-arm motion can be discriminated through leaning of single-arm motions only. In the experiments performed, the proposed method was applied to the discrimination of up to 50 dual-arm motions. The results showed that the method enables relatively high discrimination performance. In addition, the possibility of applying the proposed method for a human-robot interface was confirmed through operation experiments for the robotic manipulator using dual-arm motions.

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