Nobutaka Tsujiuchi

Development of prosthetic arm with pneumatic prosthetic hand and tendon-driven wrist

Recently, various prosthetic arms have been developed, but few are both attractive and functional. Considering human coexistence, prosthetic arms must be both safe and flexible. In this research, we developed a novel prosthetic arm with a five-fingered prosthetic hand using our original pneumatic actuators and a slender tendon-driven wrist using a wire drive and two small motors. Because the prosthetic hand’s driving source is comprised of small pneumatic actuators, the prosthetic hand is safe when it makes contact with people; it can also operate flexibly. In addition, the arm has a tendon-driven wrist to expand its motion space and to perform many operations. First, we explain the pneumatic hand’s drive mechanism and its tendon-driven wrist. Next, we identify the characteristics of the hand and the wrist and construct a control system for this arm and verify its control performance.

Development of Robot Hand with Pneumatic Actuator and Construct of Master-Slave System

Recently, research has concentrated on robots that can coexist with people and be of use to them. Such a robot needs to be both safe and flexible. Here, we use a pneumatic actuator as the driving source of a robot hand. We develop a pneumatic actuator driven by low pressure because we consider that the conventional pneumatic actuator is inadequate for the driving source of a robot hand. First, we examine the characteristics of our new pneumatic actuator. Next, we develop a five-fingered robot hand using this pneumatic actuator. The robot hand produced is both safe and flexible. We construct a master-slave system to enable the robot hand to perform the same operations as a human hand. Next, we make a joint model that has one degree of freedom using a pneumatic actuator. We construct a control system for the joint model and verify its control performance.

Development of a composite boring bar

A composite boring bar, whose stability against chattering is superior to not only conventional steel bars but also cemented carbide bars, has recently been developed. The main material of this composite bar is pitch-based carbon fiber reinforced plastic. Carbon fibers aligned unidirectionally in the longitudinal direction of the bars give high bending stiffness. Four types of bar having different shaped steel cores were designed by FEM analysis and produced for actual testing. A bar having a cross-shaped steel core shows the best cutting capability and stability amongst all bars designed. This bar can be used when the length (L) and diameter (D) ratio L/D is 7 or even at severe conditions while a cemented carbide bar cannot control the chatter vibration even if the L/D is less than 6. Emphasis should be placed on the fact that the cross-shaped steel core can increase the bending stiffness of the bar in both tangential and radial directions by constraining the shear deformation of the fiber layers without sacrificing the increase of resonant frequencies.

Development of a low pressure driven pneumatic actuator and its application to a robot hand

This paper presents a non-metallic and light robot hand that uses balloon-type pneumatic actuators that we developed. Recently, there has been a lot of research into robot coexisting with people and helping them. Such robots must be proven safe for contact with people. The drive system of our proposed robot hand uses balloon-type pneumatic actuators that we originally developed in consideration of the safety measures required for human contact. The results of test on the characteristics of the pneumatic actuator proved that it is possible to drive a robot hand and create enough generated force to grasp objects using lower inner pressure and lower air volume. Moreover, a model the same size as a human hand can be made without a big air compressor, we also consider the applications of such prosthetic hands

Development and control of pneumatic robot arm for industrial fields

A pneumatic robot arm driven by pneumatic actuators was developed as a versatile end effector for material handling systems. The arm consists of a pneumatic hand and pneumatic wrist. The hand can grasp various objects without force sensors or feedback control. Therefore, this study aims to control the wrist motions to expand the hand motions space. The hand is shaped like that of a human being, and it can grasp objects that have different shapes and mechanical characteristics. However, the wrist has redundant degrees of freedom. This is useful when the robot moves to avoid obstacles. However, the drive mechanism of the wrist has nonlinearity from a mechanical viewpoint. Also, the pneumatic actuators used as the drive source have hysteresis characteristics. These features make the wrist motions difficult to control. Because the wrist is used in material handling systems, its motions need to be freely controlled. Therefore, in this research, experimental models of the drive system of the pneumatic robot wrist have been constructed. With the constructed models, the control systems were designed through simulations. After that, we attempted to control the wrist motions with the constructed controllers. As a result, the wrist models are coincident with wrist motions. Finally, experimental results were obtained that match the simulation results.

Forearm motion discrimination technique using real-time EMG signals

The objective of this study is to develop a method of discriminating real-time motion from electromyogram (EMG) signals. We previously proposed a motion discrimination method. This method could discriminate five motions (hand opening, hand closing, hand chucking, wrist extension, and wrist flexion) at a rate of above 90 percent from four channel EMG signals in the forearm. The method prevents elbow motions from interfering with hand motion discrimination. However, discrimination processing time of this method is more than 300 ms, and the shortest delay time that is perceivable by the user is generally regarded to be roughly 300 ms. Furthermore, a robot hand has a mechanical delay time. Thus, the discrimination time should be less than 300 ms. Here, we propose a real-time motion discrimination method using a hyper-sphere model. In comparison with the old model, the hyper-sphere models can make more complex decision regions which can discriminate at the state of the motion. Furthermore, this model can learn EMG signals in real-time. We experimentally verified that the discrimination accuracies of this method were above 90 percent. Moreover, elbow motions did not interfere with the hand motion discrimination. The discrimination processing time was less than 300 ms, and was about 30 percent shorter than that of the old method.

Hand Motion Estimation by EMG Signals Using Linear Multiple Regression Models

The purpose of this research is to construct an intelligent upper limb prosthesis control system that uses electromyogram (EMG) signals. The signal processing of EMG signals is performed using a linear multiple regression model that can learn parameters in a short time. Using this model, joint angles are predicted, and the motion pattern discrimination is conducted. Discriminated motions were grip, open, and chuck of a hand. Predicted joint angles were multi-finger angles corresponding to these three motions. In several experiments we proved the usefulness of processing EMG signals with a linear multiple regression model

Manipulation of a robot by EMG signals using linear multiple regression model

In this research, a robot was operated by EMG signals using a linear multiple regression model. Myoelectric upper-limb prostheses are one example of an application that employs EMG signals as a control input. However, commercial myoelectric upper-limb prostheses can perform only grasping motions and wrist rotation. Many researches on multifunctionalization of myoelectric upper-limb prostheses have been undertaken, and pattern recognition for discriminating desired motions of hands from EMG signals have been attempted. Artificial neural networks are commonly applied in these cases. Since EMG signals have nonlinear characteristics, it is reasonable to use artificial neural networks to produce accurate nonlinear maps. However, this is not practical because large amounts of training time are necessary before actual use. In this research, signals that predict operation using our linear multiple regression models are generated, and although a learning process is also needed in this method, it takes only a short time. Using this technique, we were able to discern forearm motion and predict an elbow joint angle. The usefulness was verified by an experiment using a robot hand and a robot arm.

Development and Control of a Pneumatic Robot Arm for Industrial Fields

A pneumatic robot arm driven by pneumatic actuators was developed as a versatile end effector for material handling systems. The arm consists of a pneumatic hand and pneumatic wrist. The hand can grasp various objects without force sensors or feedback control. Therefore, this study aims to control the wrist motions to expand the hand motions space. The hand is shaped like that of a human being, and it can grasp objects that have different shapes and mechanical characteristics. However, the wrist has redundant degrees of freedom. This is useful when the robot moves to avoid obstacles. However, the drive mechanism of the wrist has nonlinearity from a mechanical viewpoint. Also, the pneumatic actuators used as the drive source have hysteresis characteristics. These features make the wrist motions difficult to control. Because the wrist is used in material handling systems, its motions need to be freely controlled. Therefore, in this research, experimental models of the drive system of the pneumatic robot wrist have been constructed. With the constructed models, the control systems were designed through simulations. After that, we attempted to control the wrist motions with the constructed controllers. As a result, the wrist models are coincident with wrist motions. Finally, experimental results were obtained that match the simulation results.

Optimization of Profile fo r Reduction of Piston Slap Excitation

This paper presents an analytical model for the prediction of piston secondary motion and the vibration due to piston slap. For the modeling of piston slap phenomenon, cylinder liner is modeled as a several spring-mass system that are connected by modal characteristics, and lubricant film between the piston and the cylinder is modeled as reaction force vectors which excite resonant mode of them. By comparing experimental results and analytical ones, the validity of the proposed model has been confirmed. The optimization of the piston skin profile is also carried out with the analytical model, and it is confirmed that the round shape of the lower part of piston skirt is effective for the reduction of piston slap excitation.