• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.258-263
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• 2014

An adaptive robot hand (AR-Hand) has a stable grasp of different objects in unstructured environments. In this study, we propose an AR-Hand based on a tendon-driven mechanism which consists of 4 fingers and 12 DOFs. It weighs 0.5 kg and can grasp an object up to 1 kg. This hand based on the adaptive grasp mechanism is able to provide a stable grasp without a complex control algorithm or sensor system. The fingers are driven by simple tendon structures with each finger capable of adaptively grasping the objects. This paper presents a method to decide the joint stiffness. The adaptive grasping is verified by various grasping experiments involving objects with different shapes and sizes.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.694-697
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• 1996

The aim of this work for 5 years from 1994 is to develop a multi-fingered robot hand and its control system for grasp and manipulation of objects dexterously. Since the robot hand is still being developed, a commercialized robot hand from Barrett Company is utilized to implement a hand controller and control algorithm. For this, VME based motion control and interface boards are developed and multi-sensors such as encoder, force/torque sensor, dynamic sensor and artificial skin sensor are partly developed and employed for the grasping control algorithm. In oder to handle uncertainties such as mechanical idleness and backlash, a fuzzy rule based grasping algorithm is also considered and tested with the developed control system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.217-218
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• 2012

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.443-444
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• 2011

• Journal of Biosystems Engineering
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• v.25 no.5
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• pp.399-408
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• 2000

This study is final focused on developing fruit harvesting robot can distinguish fruit type and status accurately. Multi-joint robot is able to discriminate tree shape and select mature fruit by image processing. The multi-joint robot consists of (a) rotating base, (b)turning first joint-arm, (c)rotating and turning second joint-arm, (d)rotating and turning third joint-arm, (e)rotating and turning last joint and (f)picker hand. The operational ranges of the robot are: horizontal 860~2,220mm, vertical 1,440~2,260mm, 270 degrees’rotation angle, 90 or 270 degrees’turning angle. The robot weighs 330kg. The multi-joint robot was designed in high accuracy and efficiency by getting as close as the movements of human arms and waist.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.328-336
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• 2012

In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.127-133
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• 2014

This paper describes a design and manufacture of a two-axis force sensor and a single-axis force sensor for the fingers of an intelligent robot's hand. The robot's finger is composed of a two-axis force sensor, a first knuckle, a single-axis force sensor, a second knuckle, a spring, a motor of first knuckle, a motor of second knuckle, and so on. The two-axis force sensor attached to the first knuckle and the single-axis force sensor attached to the second knuckle were designed and manufactured, and the characteristics test of two sensors was carried out. As a test results, the interference error of the two-axis force sensor was less than 0.68%, the repeatability error of each sensor was less than 0.02%, and then the non-linearity was less than 0.03%. It is thought that the sensors can be used for the fingers of the intelligent robot's hand for rehabilitation exercise of finger patients.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.126-131
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• 2017

In this paper, we propose a method to extract meaningful motion among various kinds of hand gestures on giving commands to robots using hand gestures. On giving a command to the robot, the hand gestures of people can be divided into a preparation one, a main one, and a finishing one. The main motion is a meaningful one for transmitting a command to the robot in this process, and the other operation is a meaningless auxiliary operation to do the main motion. Therefore, it is necessary to extract only the main motion from the continuous hand gestures. In addition, people can move their hands unconsciously. These actions must also be judged by the robot with meaningless ones. In this study, we extract human skeleton data from a depth image obtained by using a Kinect v2 sensor and extract location data of hands data from them. By using the Kalman filter, we track the location of the hand and distinguish whether hand motion is meaningful or meaningless to recognize the hand gesture by using the hidden markov model.

• Journal of the Korean Society of Industry Convergence
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• v.16 no.4
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• pp.141-145
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• 2013

This paper is the development of industrial robotic hand system and the design methods of industrial robot hand that can mimic human fingers motion. In order to overcome problems incurred during the reduction of the mobility, this study focuses on analyzing human hand structure and finger movements from an anatomical point of view. As a result, distinctive features that improve the discovered stability in constraints for range of motion in the fingers is reflected in this design concept. A 4-bar Linkage is used in robot finger structure. Lastly, there were experiments to inspect the developed robot hands performance. The developed robot hand has many potential applications and can be in many different fields.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.83.1-83
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• 2001

This paper presents a three-fingered robot hand, called the KIST hand, Which have one active joint and one passive joint. The thumb is fixed on the palm, and the index and the middle take lateral motions symmetrically. A mechanical clutch and an embedded force sensor, attached on the distal link of the fingers, enable the KIST hand to perform human-like functions. A result of experiment shows reliable grasping performance of the hand which maintain stable grasp under disturbances.