• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.359-368
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• 2001

A new knee-ankle-foot-orthosis(KAFO) which uses an automatically-controlled electromechanical wrap spring clutch for the knee joint was developed in the present study. It was found that the output voltage from the foot switches of the developed KAFO was proportionally increased with respect to the applied load. The output voltage from the infrared sensor also decreased as the knee flexion angle increased. The knee joint system for the new KAFO weighs only 780g lighter than any other commercially available developed system. In addition, the solenoid reduces the reaction time for the automatic control of the knee joint. The static torque of the clutch was measured for three persons, and it satisfied the normal knee extension moment during the pre-swing. Three-dimensional gait analyses for three different gait patterns (normal gait, locked-knee gait, controlled-knee gait) from five normal subjects were conducted. Controlled-knee gait showed the maximum knee flexion angle of 40.56$\pm9.55^{\circ}$ and the maximum knee flexion moment of 0.20$\pm$0.07Nm/kg at similar periods in the normal gait. Our KAFO system satisfies both stability during stance phase and free knee flexion during the swing phase at the proper period during the gait cycle. Therefore, our KAFO system would be very useful in various low extremity orthotic applications.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.243-251
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• 2014

In inference fit assembly process of the industrial robot, it basically needs the force data. One of the typical methods to get the force data is attaching torque sensors on the robot arm joint or end effector. This is effective way to reduce time delay and to improve preciseness of force control, but this method has several problems. To solve that problem, this paper suggests method which measures assembly force without torque sensor by using the sliding perturbation observer(SPO) and assembly process based on SPO to assemble successfully in inference assembly

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.286-292
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• 2009

This paper proposes a knee-wearable robot system for assisting the muscle power of human knee by processing EMG (Electromyogram) signals. Although there are many muscles affecting the knee joint motion, the rectus femoris and biceps femoris among them play a core role in the extension and flexion motion, respectively, of the knee joint. The proposed knee-wearable robot system consists of three parts; the sensor for measuring and processing EMG signals, controller for estimating and applying the required knee torque, and actuator for driving the knee-wearable mechanism. Ultimately, we suggest the motion control method for knee-wearable robot system by processing the EMG signals of corresponding two muscles in this paper. Also, we show the effectiveness of the proposed knee-wearable robot system through the experimental results.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.1
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• pp.37-43
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• 2010

In this paper, we implement that hollow type joint using two more than general motor and design for compact structure embedded electronic parts in the development of 7 degree of freedom manipulator. We propose a method to overcome risk and the limit of operating radius which are point out as a limit of previous industrial robot. and also propose to more efficient and stable manipulator implement method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1140-1146
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• 1999

In this paper, a force estimation method is proposed for force control without force sensor. For this , a disturbance observer is applied to each joint of an {{{{ { n}_{ } }}}} degrees of freedom manipulator to obtain a simple equivalent robot dynamics(SERD) being represented as an n independent double integrator system. To estimate the output of disturbance observer due to internal torque, the disturbance observer output estimator(DOOE) is designed, where uncertain parameters of the robot manipulator are adjusted by the gradient method to minimize the performance index which is defined as the quadratic form of the error signal between the output of disturbance observer and that of DOOE. when the external force is exerted, the external force is estimated by the difference between the output of disturbance observer and DOOE, since output of disturbance observer includes the external torque signal as well as the internal torque estimated by the output of DOOE. And then, a force controller is designed for force feedback control employing the estimated force signal. To verify the effectiveness of the proposed force estimation method, several numerical examples and experimental results are illustrated for the 2-axis direct drive robot manipulator.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.81-88
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• 2006

Control of a robot manipulator in contact with the environment is usually conducted by the direct feedback control using a force-torque sensor or the indirect impedance control. In these methods, however, the control algorithms become complicated and the performance of position and force control cannot be improved because of the mechanical properties of the passive components. To cope with such problems, redundant actuation has been used to enhance the performance of position control and force control. In this research, a Double Actuator Unit (DAU) is proposed, with which the force control algorithm can be simplified and can make the robot ensure the safety during the external collision. The DAU is composed of two actuators; one controls the position and the other modulates the joint stiffness. Using this unit, it is possible to independently control the position and stiffness. The DAU based on the planetary gears is investigated in this paper. Performance using the DAU is also verified by various experiments. It is shown that the manipulator using this mechanism provides better safety during the impact with the environment by reducing the joint stiffness appropriately on detecting the collision of a manipulator.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1831_1832
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• 2009

본 논문에서는 유연관절 로봇 팔 제어를 위한 토크센서 기반의 외란에 강인한 제어기 설계를 다루고 있다. 로봇은 관절의 토크센서를 통해 관절에서 발생하는 토크의 측정이 가능하며 외란에 강인한 제어기 설계를 위해 외란 관측기가 적용 되었다. 외란관측기는 시스템에 작용하고 있는 외란을 상쇄하는 역할을 한다. 본 논문에서 설계된 제어기의 성능은 컴퓨터 모의실험을 통하여 확인하도록 한다.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.1
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• pp.50-53
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• 2014

Although there are some calculations of kinetics, dynamics, torque of each joint, size and weight which are used in implementing of humanoid robot, it is too expensive and need much education to make frame of robot body, actuator, and etc. Moreover, since there is lots of differences of operational principle, we need many kinds of experimental and education. However, the real humanoid robot is difficult to propagate because of its prices and other technical problems. Therefore we need small robot platform and control method which can give a enough education effect as similar as real humanoid robot. In this paper, the Kinect Sensor which made by Microsoft will be used for control method of humanoid platform.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.85-92
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• 2024

In this paper, we developed an intuitive teaching and control system that directly teaches a task by holding the tip of a robotic arm and moving it to a desired position. The developed system consists of a 6-axis force sensor that measures position and attitude forces at the tip of the robot arm, an algorithm for generating robot arm joint speed control commands based on the measured forces at the tip, and a self-made 6-axis robot arm and control system. The six-dimensional force/torque of the position posture of the robot arm operator steering the handler is detected by the force sensor attached to the handler at the leading edge and converted into velocity commands at the leading edge to control the 7-axis robot arm. The verification of the research method was carried out with a self-made 7-axis robot, and it was confirmed that the proposed force sensor-based robot end-of-arm control method operates successfully through experiments by teaching the operator to adjust the handler.