• Title/Summary/Keyword: Robot's Finger

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The Gripping Force Control of Robot Manipulator Using the Repeated Learning Function Techniques (반복 학습기능을 이용한 로봇 매니퓰레이터의 파지력제어)

  • Kim, Tea-Kwan;Baek, Seung-Hack;Kim, Tea-Soo
    • Journal of the Korean Society of Industry Convergence
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    • v.18 no.1
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    • pp.45-52
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    • 2015
  • In this paper, the repeated learning technique of neural network was used for gripping force control algorithm. The hybrid control system was introduced and the manipulator's finger reorganized form 2 ea to 3 ea for comfortable gripping. The data was obtained using the gripping force of repeated learning techniques. In the fucture, the adjustable gripping force will be obtained and improved the accuracy using the artificial intelligence techniques.

A Study on the Design and Implementation of a Camera-Based 6DoF Tracking and Pose Estimation System (카메라 기반 6DoF 추적 및 포즈 추정 시스템의 설계 및 구현에 관한 연구)

  • Do-Yoon Jeong;Hee-Ja Jeong;Nam-Ho Kim
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.5
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    • pp.53-59
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    • 2024
  • This study presents the design and implementation of a camera-based 6DoF (6 Degrees of Freedom) tracking and pose estimation system. In particular, we propose a method for accurately estimating the positions and orientations of all fingers of a user utilizing a 6DoF robotic arm. The system is developed using the Python programming language, leveraging the Mediapipe and OpenCV libraries. Mediapipe is employed to extract keypoints of the fingers in real-time, allowing for precise recognition of the joint positions of each finger. OpenCV processes the image data collected from the camera to analyze the finger positions, thereby enabling pose estimation. This approach is designed to maintain high accuracy despite varying lighting conditions and changes in hand position. The proposed system's performance has been validated through experiments, evaluating the accuracy of hand gesture recognition and the control capabilities of the robotic arm. The experimental results demonstrate that the system can estimate finger positions in real-time, facilitating precise movements of the 6DoF robotic arm. This research is expected to make significant contributions to the fields of robotic control and human-robot interaction, opening up various possibilities for future applications. The findings of this study will aid in advancing robotic technology and promoting natural interactions between humans and robots.

Morphometric Study on the Arterial Palmar Arch of the Hand (손바닥 동맥활에 관한 형태계측 연구)

  • Park, Bong Kwon;Jang, Soo Won;Choi, Seung Suk;Ahn, Hee Chang
    • Archives of Plastic Surgery
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    • v.36 no.6
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    • pp.691-701
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    • 2009
  • Purpose: Deviations of arterial palmar arches in the hand can be explained on the embryological basis. The purpose of this study was to provide new information about palmar arches through cadaver's dissection. The values of the location and diameter in these vessels were analyzed in order to support anatomical research and clinical correlation in the hand. Methods: The present report is based on an analysis of dissections of fifty - three hands carried out in the laboratory of gross anatomy. A reference line was established on the distal wrist crease to serve as the X coordinate and a perpendicular line drawn through the midpoint between middle and ring fingers, which served as the Y coordinate. The coordinates of the x and y values were measured by a digimatic caliper, and statistically analyzed with Student's t - test. Results: Complete superficial palmar archs were seen in 96.2 % of specimens. In the most common type of males, the superficial arch was formed only by the ulnar artery. In the most common type of females, the superficial arch was formed anastomosis between the radial artery and the ulnar artery. The average length of the superficial and deep palmar arch is $110.3{\pm}33.0mm$ and $67.9{\pm}14.0mm$ respectively. Regarding the superficial palmar arch, ulnar artery starts $-16.1{\pm}5.1mm$ on X - line, and $2.5{\pm}24.5mm$ on Y - line. Radial artery appears on palmar side $7.7{\pm}3.2mm$ on X - line, and $20.9{\pm}10.9mm$ on Y - line. But radial artery starts on $6.3{\pm}3.6mm$ on X - line, and $3.4{\pm}5.1mm$ on Y - line. Digital arteries of superficial palmar arch starts on $6.1{\pm}3.7mm$, $33.9{\pm}8.8mm$ on index finger, $1.8{\pm}3.4mm$, $40.1{\pm}7.3mm$ on middle finger, $-3.2{\pm}4.9mm$, $42.6{\pm}7.0mm$ on ring finger, and $-8.9{\pm}5.1mm$, $42.5{\pm}80mm$ on little finger in respective X and Y coordinates. Radial artery of deep palmar arches measured at the palmar side perforating from the dorsum of hand. It's coordinates were $9.7{\pm}4.8mm$ on X - line, $21.7{\pm}10.2mm$ on Y - line. Ulnar artery was measured at hypothenar area, and it's coordinates were $-20.4{\pm}6.3mm$ on X - line, and $30.6{\pm}7.4mm$ on Y - line. Conclusions: Anatomically superficial palmar arch can be divided into a complete and an incomplete type. Each of them can be subdivided into 4 types. The deep palmar arch is less variable than the superficial palmar arch. We believe these values of the study will be used for the vascular surgery of the hand using the endoscope and robot in the future.

Sensor-based Recognition of Human's Hand Motion for Control of a Robotic Hand (로봇 핸드 제어를 위한 센서 기반 손 동작 인식)

  • Hwang, Myun Joong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.9
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    • pp.5440-5445
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    • 2014
  • Many studies have examined robot control using human bio signals but complicated signal processing and expensive hardware are necessary. This study proposes a method to recognize a human's hand motion using a low-cost EMG sensor and Flex sensor. The method to classify movement of the hand and finger is determined from the change in output voltage measured through MCU. The analog reference voltage is determined to be 3.3V to increase the resolution of movement identification through experiment. The robotic hand is designed to realize the identified movement. The hand has four fingers and a wrist that are controlled using pneumatic cylinders and a DC servo motor, respectively. The results show that the proposed simple method can realize human hand motion in a remote environment using the fabricated robotic hand.

The Kinematical Characteristics of the Basic Ballet Position (발레에서 팔 기본 동작의 운동학적 특성)

  • Kim, Eun-Hee
    • Korean Journal of Applied Biomechanics
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    • v.16 no.1
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    • pp.151-158
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    • 2006
  • The purpose of this study was to find out the kinematical characteristics of arm's basic position in ballet. In order to achieve the purpose of the study, 3D cinematographic analysis was conducted with a ballerina who might performed the perfect arm's basic position. According to the results of this study, it was appeared that the shoulder kept about 78%-82%, the elbow kept about 62%-96%, the wrist kept 52%-109%, and finger kept 48%-110% with the height. Also, movement was formed with $21^{\circ}-77^{\circ}$ of the upper arm angle, $106^{\circ}-164^{\circ}$ of the elbow, $125^{\circ}-140^{\circ}$ of the wrist, and $83^{\circ}-160^{\circ}$ of the shoulder. The left-right ratio of the total arm angle was 98% in the first, second, and third position, and 100% in the forth position. The angle of arm gradient was remained $-68^{\circ}$ in the first position, $-27^{\circ}$ in the second position, $73^{\circ}$ in the third position, and $-11^{\circ}$ in the forth position. Based on the results mentioned above, balance and symmetry of both arms was an important factor in those four positions. Although it is impossible to maintain the position like robot, it may be a good performance if a certain level of extent was remained With respect to this point of view, it may be a good position if the difference between right and left arm in each joint can be remained within 2%. Angle also was an important factor that if the difference in total angle can be remained within 2% it may be an excellent position, there was difference of right and left based on the joint though. Therefore, practice and instruction to make a perfect symmetry as much as possible were needed Also, it would be a good movement if position and angle of joint within 2% difference of right and left arm can be remained In turn, because ballet is movement with expression of the body, beauty of the body and balance of the movement have to be harmonized for beautiful performance. Therefore, it would be a meaningful future study considering the body condition and movement of ballerina to define the beauty.

Light Modulation based on PPG Signal Processing for Biomedical Signal Monitoring Device (생체 정보 감시 장치를 위한 광변조 기법의 PPG 신호처리)

  • Lee, Han-Wook;Lee, Ju-Won;Jeong, Won-Geun;Kim, Seong-Hoo;Lee, Gun-Ki
    • Journal of Biomedical Engineering Research
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    • v.30 no.6
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    • pp.503-509
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    • 2009
  • The development of technology has led to ubiquitous health care service, which enables many patients to receive medical services anytime and anywhere. For the ubiquitous health care environment, real-time measurement of biomedical signals is very important, and the medical instruments must be small and portable or wearable. So, such devices have been developed to measure biomedical signals. In this study, we develop the biomedical monitoring device which is sensing the PPG signal, one of the useful signal in the field of ubiquitous healthcare. We design a watch-like biomedical signal monitoring system without a finger probe to prevent the user's inconvenience. This system obtains the PPG from the radial artery using a sensor in the wrist band. But, new device developed in this paper is easy to get the motion artifacts. So, we proposed new algorithm removing the motion artifacts from the PPG signal. The method detects motion artifacts by changing the degree of brightness of the light source. If the brightness of the light source is reduced, the PPG pulses will disappear. When the PPG pulses have disappeared completely, the remaining signal is not the signal that results from the changing blood flow. We believe that this signal is the motion artifact and call it the noise reference signal. The motion artifacts are removed by subtracting the noise reference signal from the input signal. We apply this algorithm to the system, so we can stabilize the biomedical monitoring system we designed.