scholarly journals Development of a High-Speed, Low-Latency Telemanipulated Robot Hand System

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 41
Author(s):  
Yuji Yamakawa ◽  
Yugo Katsuki ◽  
Yoshihiro Watanabe ◽  
Masatoshi Ishikawa
Keyword(s):  
High Speed ◽  
Vision System ◽  
Low Latency ◽  
Human Hand ◽  
Robot Hand ◽  
Hand Motion ◽  
Visual Sensing ◽  
System A ◽  
Robot Hands ◽  
3D Sensing

This paper focuses on development of a high-speed, low-latency telemanipulated robot hand system, evaluation of the system, and demonstration of the system. The characteristics of the developed system are the followings: non-contact, high-speed 3D visual sensing of the human hand, intuitive motion mapping between human hands and robot hands, and low-latency, fast responsiveness to human hand motion. Such a high-speed, low-latency telemanipulated robot hand system can be considered to be more effective from the viewpoint of usability. The developed system consists of a high-speed vision system, a high-speed robot hand, and a real-time controller. For the developed system, we propose new methods of 3D sensing, mapping between the human hand and the robot hand, and the robot hand control. We evaluated the performance (latency and responsiveness) of the developed system. As a result, the latency of the developed system is so small that humans cannot recognize the latency. In addition, we conducted experiments of opening/closing motion, object grasping, and moving object grasping as demonstrations. Finally, we confirmed the validity and effectiveness of the developed system and proposed method.

Optimized Fingertip Mapping:A General Algorithm for Robotic Hand Teleoperation

1993 ◽  
Vol 2 (3) ◽  
pp. 203-220 ◽  
Author(s):  
Robert N. Rohling ◽  
John M. Hollerbach ◽  
Stephen C. Jacobsen
Keyword(s):  
General Algorithm ◽  
Human Hand ◽  
Robotic Hand ◽  
Robot Hand ◽  
Mapping Strategy ◽  
Robot Hands ◽  
A Priority ◽  
Dextrous Hand

An optimized fingertip mapping (OFM) algorithm has been developed to transform human hand poses into robot hand poses. It has been implemented to teleoperate the Utah/MIT Dextrous Hand by a new hand master: the Utah Dextrous Hand Master. The keystone of the algorithm is the mapping of both the human fingertip positions and orientations to the robot fingers. Robot hand poses are generated by minimizing the errors between desired human fingertip positions and orientations and possible robot fingertip positions and orientations. Differences in the fingertip workspaces that arise from kinematic dissimilarities between the human and robot hands are accounted for by the use of a priority based mapping strategy. The OFM gives first priority to the human fingertip position goals and the second to orientation.

Kinematic Analysis of an Anthropomorphic Robot Hand

2012 ◽  
Vol 187 ◽  
pp. 293-297
Author(s):  
Pramod Kuma Parida ◽  
Bibhuti Bhusan Biswal ◽  
Dhirendra Nath Thatoi
Keyword(s):  
Kinematic Analysis ◽  
Degrees Of Freedom ◽  
Industrial Applications ◽  
Human Hand ◽  
Robot Hand ◽  
Orthopedic Rehabilitation ◽  
Robot Hands

There has been a continuous effort by researchers to develop multi-fingered robot hands for variety of applications. Some of these hands are meant for industrial applications while thers are used for orthopedic rehabilitation of humans. However the degree of success to develop an anthropomorphic robot hand in close resemblence with a typical human hand has not been satisfactory. In the present work an attempt has been made to design a robot hand having five fingers with 25 degrees of freedom by closly following the anatomy of human hand.The kinematic analysis of the hand offers confirmative results for effective graspingand manipulating objects.

scholarly journals Development of a Real-Time Human-Robot Collaborative System Based on 1 kHz Visual Feedback Control and Its Application to a Peg-in-Hole Task

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 663
Author(s):  
Yuji Yamakawa ◽  
Yutaro Matsui ◽  
Masatoshi Ishikawa
Keyword(s):  
Real Time ◽  
High Speed ◽  
High Accuracy ◽  
Robot Hand ◽  
Collaborative System ◽  
Visual Sensing ◽  
Hand Control ◽  
The Stability ◽  
Visual Feedback Control ◽  
Human Robot Collaboration

In this research, we focused on Human-Robot collaboration. There were two goals: (1) to develop and evaluate a real-time Human-Robot collaborative system, and (2) to achieve concrete tasks such as collaborative peg-in-hole using the developed system. We proposed an algorithm for visual sensing and robot hand control to perform collaborative motion, and we analyzed the stability of the collaborative system and a so-called collaborative error caused by image processing and latency. We achieved collaborative motion using this developed system and evaluated the collaborative error on the basis of the analysis results. Moreover, we aimed to realize a collaborative peg-in-hole task that required a system with high speed and high accuracy. To achieve this goal, we analyzed the conditions required for performing the collaborative peg-in-hole task from the viewpoints of geometric, force and posture conditions. Finally, in this work, we show the experimental results and data of the collaborative peg-in-hole task, and we examine the effectiveness of our collaborative system.

scholarly journals Hand Gesture Controlling using Artificial Intelligence

2019 ◽  
Vol 8 (12) ◽  
pp. 859-862
Keyword(s):  
Artificial Intelligence ◽  
Hand Gesture ◽  
Human Hand ◽  
Medical Procedure ◽  
Robot Hand ◽  
Hand Motion ◽  
Low Dimension ◽  
Hand Signal ◽  
Gaming Machines ◽  
Minimal Effort

Presently multi day's robot is constrained by remote or mobile phone or by direct wired association. In the event that we pondering expense and required equipment, this things builds the unpredictability, particularly for low dimension application. Presently the robot that we have structured is not quite the same as over one. It doesn't require any kind of remote or any correspondence module. it is a self-enacted robot, which drives itself as indicated by the position of a client who remains before it. It does what the client wants to do. it makes a duplicate, all things considered, development of the client remaining before it. Equipment required is little, and henceforth minimal effort and little in size. Of late, there has been a flood in enthusiasm for perceiving human Hand signal controlled robot. Hand motion acknowledgment has a few uses, for example, PC amusements, gaming machines, as mouse substitution and apparatus controlled robot (for example crane, medical procedure machines, apply autonomy, counterfeit intelligence

Analysis and Systematic Classification of Human Hand Movement for Robot Hand Design

2008 ◽  
Vol 20 (3) ◽  
pp. 429-435 ◽  
Author(s):  
Takeshi Ninomiya ◽  
◽  
Takashi Maeno ◽  
Keyword(s):  
Hand Movement ◽  
Hand Movements ◽  
Human Hand ◽  
Robot Hand ◽  
Systematic Classification ◽  
Robot Hands ◽  
Human Muscles ◽  
Set Up ◽  
Human Hands

The systematic classification of hand movements, which indicates the minimum mechanism of robot hands, is suggested. The performance of existent robot hands is not as high as that of human hands because the performance of existent actuators does not come up to that of human muscles in the same volume. It is important for robot hands to accomplish targeted tasks with a minimum mechanism. Human hand movements are analyzed quantitatively considering robot hands such as associated movement of DIP and PIP joints. Based on the results of analysis, we obtain three items, i.e., fingers, joints that must be set up actuators and basic movements we define. We systematically classify human hand movement for the robot hand based on three items.

Visual-Servoing Control of Robot Hand with Estimation of Full Articulation of Human Hand

2014 ◽  
Vol 625 ◽  
pp. 728-735
Author(s):  
Motomasa Tomida ◽  
Kiyoshi Hoshino
Keyword(s):  
High Speed ◽  
Visual Servoing ◽  
Image Features ◽  
Human Hand ◽  
Depth Camera ◽  
Robotic Hand ◽  
Robot Hand ◽  
Depth Sensor ◽  
Data Set ◽  
Hand Image

A depth sensor or depth camera is available at a reasonable cost in recent years. Due to the excessive dispersion of depth values outputted from the depth camera, however, changes in the pose cannot be directly employed for complicated hand pose estimation. The authors therefore propose a visual-servoing controlled robotic hand with RGB high-speed cameras. Two cameras have their own database in the system. Each data set has proportional information of each hand image and image features for matching, and joint angle data for output as estimated results. Once sequential hand images are recorded with two high-speed RGB cameras, the system first selects one database with bigger size of hand region in each recorded image. Second, a coarse screening is carried out according to the proportional information on the hand image which roughly corresponds to wrist rotation, or thumb or finger extension. Third, a detailed search is performed for similarity among the selected candidates. The estimated results are transmitted to a robot hand so that the same motions of an operator is reconstructed in the robot without time delay.

Control of a Biomimetic Robot Hand Finger

2015 ◽  
pp. 475-499
Author(s):  
Yunus Ziya Arslan ◽  
Yuksel Hacioglu ◽  
Yener Taskin ◽  
Nurkan Yagiz
Keyword(s):  
Sliding Mode ◽  
Control Strategies ◽  
Metabolic Energy ◽  
Human Hand ◽  
Robot Hand ◽  
Robotic Hands ◽  
Dexterous Manipulation ◽  
Model And Simulation ◽  
Robot Hands ◽  
Biomimetic Robot

Due to the dexterous manipulation capability and low metabolic energy consumption property of the human hand, many robotic hands were designed and manufactured that are inspired from the human hand. One of the technical challenges in designing biomimetic robot hands is the control scheme. The control algorithm used in a robot hand is expected to ensure the tracking of reference trajectories of fingertips and joint angles with high accuracy, reliability, and smoothness. In this chapter, trajectory-tracking performances of different types of widely used control strategies (i.e. classical, robust, and intelligent controllers) are comparatively evaluated. To accomplish this evaluation, PID, sliding mode, and fuzzy logic controllers are implemented on a biomimetic robot hand finger model and simulation results are quantitatively analyzed. Pros and cons of the corresponding control algorithms are also discussed.

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