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A gait training method that can adjust the target leg load is required to meet the varied demands of trainees. In this study, we propose a running training method using a soft gait-assistive suit—a power-assistive device that adds an external force to the leg of a trainee by actuating a pneumatic artificial muscle (PAM). Herein, the assisting force is applied to the leg of the trainee so that muscle activity is reduced throughout the gait cycle. We detected the terminal stance phase as an appropriate assist timing from the pressure information, and the detected timing was used for the PAM control. We conducted a verification experiment of the PAM pressure variation. In a running mode of 7.0 to 10.0 km/h, all data showed that the PAM at the calf front has better detectability than that at the calf back, which is used in conventional detection methods. The experimental results confirmed that the PAM for gait phase detection should be positioned at the calf front during running. The effectiveness of the proposed running training method was verified through experiments on four trainees using a treadmill. The leg electromyogram of the trainees was measured and changes observed. Accordingly, 14 out of 16 channels of the four trainees showed a significant difference, and the electromyograms decreased by the suit assistance in the range of −1.4 to − 80.3% maximum voluntary contraction. Thus, the experimental results quantitatively confirmed that the proposed running assistance method is effective in reducing muscular activity.

Considering the widespread use of surgical robots in recent years, computer-assisted surgery is becoming significantly popular. Because the automation of surgical tasks without human intervention remains complex owing to individual patient differences, a human cooperative control is proposed in this study. A system in which an operator manipulates one surgical instrument to insert a suture needle was developed, along with another surgical instrument that automatically pulls out the needle from the operated instrument. In the proposed method, YOLOv3 and a standard convolutional neural network (CNN) are used to estimate the penetration and pull state of the needle. An image-based state estimator classifies the state regardless of the stiffness of the object to which the suture needle is inserted. Furthermore, after the pull state is detected, despite a failure of the needle pulling, the position can be corrected and the automated surgical instrument can approach the needle again. Experiments on human cooperative control demonstrated the effectiveness of state estimation using the proposed method. In addition, the failure of grasping observed in a previous study caused by the needle angle error was reduced.

Various applications using pneumatic artificial muscles (PAMs) have been developed in recent years. When the pressure of the PAM is controlled, it is desirable to position a pressure sensor at the control port of a servo valve through a pipeline for improvement in usability, environment resistance, and elimination of mechanical complexity. However, the responsiveness of the pressure control worsens due to the volume and pressure losses through the pipeline. In this paper, we propose a pneumatic circuit model to compensate for such losses through the pipeline and a method to estimate the pressure at the PAM inlet. The proposed method improves the responsiveness of the pressure control by positioning a pressure sensor at the control port of the servo valve. We developed an experimental apparatus that simulates the mechanism of our power assist robot, and it was used for performance evaluation experiments of the conventional and proposed pressure control methods. When a sinusoidal pressure target value with a frequency of 1.5 Hz and load mass of 8 kg was fed as input, the errors between the measured and target values in the conventional and proposed control were approximately 45 and 20 kPa, respectively. The reduced error confirmed that the responsiveness of the pressure control was improved by the proposed method.

In laparoscopic surgery, the laparoscope is controlled by a camera assistant according to verbal instructions from the surgeon. A laparoscope holder is effective to provide stable view. However, the surgeon must control the holder. The autonomous control of the holder is helpful to the surgeon to focus on the surgery. The forceps tip in laparoscopic image becomes the target of the automatically controlled robot. Estimating the future movement of the forceps can improve the control performance of the holder robot. In this paper, we propose a method to estimate the forceps tip motion 0.1 seconds ahead by deep learning using the segmented forceps in camera image. The center of gravity of the gripper in the forceps was calculated from the segmented image. The future coordination of the forceps gravity was estimated by deep learning using past gravity positions. The neural network structure of deep learning was determined by comparing computational errors. We experimentally confirmed that the forceps movement of 0.1 seconds ahead can be estimated online in pick-and-place and suture tasks.

In this study, we developed a selective driving joint forceps (SDJF) for laparoscopic surgery. The SDJF has a mechanism that the driving joints can be selected arbitrarily, therefore each joint doesn’t require an individual actuator for operating. The developed SDJF has six joints that can be operated using only four actuators. Each joint has 2-degrees-of-freedom (DOF) of flexion. Therefore, the SDJF has the same working area as the forceps having six driving joints (each joints can bend ±30° around the X and Y axes). The mechanism of the SDJF is realized by fixing each joint with a collar made of shape memory polymer. The proposed mechanism not only reduces the number of actuators required for joint operation, but also has the rigidity of the forceps, which is important in surgery. In addition, a driving section of the forceps is driven by pneumatic cylinders, therefore, the forceps joint has high-back-drivability, lightweight and high-output. We measured the heating and cooling time required to change the driving joint, dynamic response and rigidity of the prototype SDJF.

In this paper, we proposed a prototype of an assistant robot for catheter ablation surgery which is one of tachyarrhythmia therapy. The proposed robot grips and operates a balloon catheter using pneumatic force. The proposed robot is possible to sense small contact force returned to the catheter by measuring pneumatic pressure and display haptic information to the user. By getting haptic information in addition to visual information from X-ray fluoroscope, surgical accuracy will be improved. From the safety point of view, we proposed an anomaly detection method of the balloon motion based on template matching. We designed and manufactured the prototype of the proposed robot which has a catheter gripping mechanism and a pushing mechanism. In addition, we carried out an animal experiment and confirmed that the proposed robot can achieve a pulmonary vein blocking task, which is an important task in catheter ablation surgery. Furthermore, we conducted an experimental validation of the anomaly detection using a removed pig heart and confirmed that the proposed detection method will be effective.

In this paper, we analyzed the stability of a wave-variables-based bilateral control system driven by pneumatic actuators. A pneumatic system is generally a third-order lag system due to the compressibility of gas. Frequency domain passivity of the system is evaluated using scattering matrix. The passivity is numerically confirmed when an appropriate control gain is given. The pneumatic system is nonlinear and can be unstable if the stability analysis is based on frequency domain passivity condition. The simulation results show that the pneumatic bilateral control system is passive.

A method to improve quality of life of elderly people is to increase muscle strength. The purpose of this study is to develop a machine to improve muscle strength effectively. In this paper, we proposed a muscle strength training machine that exerts variable resistance using pneumatic artificial rubber muscle. In addition, we observed the effect on muscle activity by using the proposed machine with predictable and unpredictable load change patterns. Experimental results showed that there was a difference between the rising time of muscle activity and the maximum amplitude of electromyogram depending on predictability of the load change and showed the possibility of effective muscle strength improvement against unpredictable load using the arm curl machine.

In this paper, we develop an interface for remote control of robots with the operator’s face movement using facial feature point detection. 14 feature points of the operator’s face are detected using a camera. The position and posture of the face is obtained solving the perspective-n-point (PnP) problem. The interface allows three degrees of freedom (DOFs) of a robot, right and left, up and down, and zoom in and out. A laparoscope holder robot is controlled by four interfaces: the proposed method, pressing buttons on the robot, using a gyroscope with foot pedal and using an image processing of a maker. The operation time using the proposed interface is almost the same as the other three interfaces.

In this study, a robotic forceps with a wrist joint using visual SLAM for joint angle sensing was developed. The forceps has a flexible joint connected to the wrist joint at its rear end and the motion of the rear joint is driven by a parallel linkage. A monocular camera attached on the rear of the parallel linkage is in charge of position sensing, and the joint angles are estimated from the pose of the camera. The pose of the camera is obtained by a visual SLAM. The visual servo system realizes a simple attaching mechanism. The static and dynamic positioning experiments are conducted. We confirmed that the visual servoing system controls the forceps tip within the error of 3 deg in the motion range of 50 deg.

Motor skill learning is fundamental in many physical activities of human. In the processes of learning of motor skills, learners often receive visual or physical information about postures from teachers. However, the information about postures usually cannot be transmitted precisely. In this paper, we propose a motion teaching system to transmit teachers' motion to learners directly by using a motion capture and an assist suit. The assist suit, which has a pneumatic artificial rubber muscle (PARM) as an actuator, was designed to move a learner's hip joint with less loss of assistive force and less constraint of motion. Hip joint motion of a teacher can be transmitted to the assist suit by master-slave control. In addition, to compensate the delay of the PARM, posture of the teacher is predicted before the occurence by a recurrent neural network by using electromyogram signals and the past joint angle. We confirmed the system can transmit a teacher's motion to a learner in real time, and with the neural network, the delay of the learner's motion could be suppressed to approximately 0.1s, which is enough to feel visual and physical information synchronous. Therefore, the proposed motion teaching system would have the ability to transmit teachers' motion to learners visually and physically with precision sufficient to facilitate skill transmission.

In this paper, a robotic forceps driven by pneumatic soft actuators is developed. The actuator consists of a soft tube made of silicone rubber covered with a metal spring. The spring acts to restrain expansion of the actuator in the radial direction during pressurization and to generate pulling force during depressurization. Furthermore, the use of the spring facilitates the manufacture of the actuator. The forceps has two degrees of freedom (Dof) which are tendon driven by four wires. Each wire is connected to the actuator via a crank shaft mechanism that converts pushing force to pulling force. An actuator is connected to the wire located at the center of the forceps for grasping. The bending angle of the forceps is estimated by using the pressure in the actuator. Therefore, the forceps is washable and sterilizable and is suitable for disposable use because the sensors are not placed on the forceps. The position control performance of the forceps is confirmed experimentally by comparing with a non-contact sensor.

In this paper, a pinch-type servo valve embedded in a pneumatic artificial rubber muscle is developed. The valve has 3-ports, consists of two flow paths with silicon tubes. Two balls are in contact with the tubes and a rotary cam pushes the balls to control the flow direction and the flow rate. The cam is controlled by an electrical motor. Since there is no pipeline to connect the valve to the pneumatic artificial rubber muscle, the pressure loss is negligible in the proposed system. Therefore, the pneumatic driven system is miniaturized and is suitable for a wearable assistive device which requires an actuator with light-weight and high power-weight ratio. We design the valve which is 105mm in length and 34mm in width. The static characteristics of the valve is measured. The compensation method of the lap condition is proposed. The pressure control experiments using the compensation are executed. The effectiveness of the valve is demonstrated by the experiments.

In this paper, a device is developed to create workspace in an abdominal cavity for laparoscopic surgery. The proposed device expands the workspace by sucking the skin with cups and lifting the cups with pneumatically driven soft actuators. The advantages of the proposed device are discussed in comparison with former methods. Then, the design and fabrication of the cup and the soft actuator is explained. The cups are made of polylactic acid (PLA) by a 3D printer. The actuators are made of silicone, poured into a mold and condensation-cured. We evaluate the static characteristics of the soft actuator and the output force of the cup. The effectiveness of the device is demonstrated experimentally by lifting a balloon.

This paper proposes a particular case of a robot body design method which determines a degrees of freedom (DOFs) number and link parameters to maximize a target task performance. The DOFs number is an essential point to be considered in the robot body design problem. In this paper, the target task is to make a long throw, and multi DOFs ball throwing robot is designed. Design parameters are the robot body parameters and its motion pattern, and they are designed to maximize ball flying distance under long throw task conditions. To define the link lengths and the robot DOFs number as design parameters, it is assumed that intermediate links of the robot have identical actuators, and these link parameters are defined as functions of link lengths. These links are chained to construct the whole link system. Because of this assumption, the motion equation, which is utilized in the task conditions, is determined by the given robot DOFs number and link lengths. The proposed method was applied to the ball throwing robot model, and its body parameters and motion pattern were designed in the proposed calculation algorithm. As a result, 5 DOFs robot and its throwing motion were obtained, and the ball flying distance was maximized. The ball flying distance was changed along with the DOFs number, and the effectiveness of the proposed design method was demonstrated.

This paper presents an integrated robot design method considering feasible motion conditions and dynamical relations between body parameters. The robot motion is generated from a vector field which is formed as a result of the interactions between a robot body, an environment and a control system. Therefore, not only the motion pattern, but also the robot body parameters should be simultaneously designed to maximize a motion performance of the robot. The integrated robot design is a method which designs a robot body and a robot motion simultaneously. The design parameters are the motion pattern and the robot body parameters, and these initial values are optimized by a gradient method to maximize (or minimize) a given evaluation function which defines the robot motion performance. Limitations of angle, angular velocity and torque are also considered, and these are utilized as the constraints to obtain the feasible robot motion. To validate the effectiveness of the proposed method, for example, a ball throwing motion performed by a human is transformed to a motion pattern of the 9 degrees of freedom (DOFs) arm robot, and the robot body parameters are designed simultaneously. In this paper, the integrated robot design method is improved, for example, additional feasible motion conditions are introduced in the optimization procedure, and dynamical relations between the robot body parameters are considered. In this case, the new motion conditions contain an orthogonal condition between a throwing direction and a hand opening direction, a static condition of a motion and a self collision avoidance condition. The dynamical relations between body parameters, which contain link length, mass, center of gravity and moment of inertia, are considered. The effectiveness of these improvement are discussed from the calculation results.

This paper proposes an off-line periodic motion pattern design method using dimensional reduction. A human periodic motion is measured by a motion capture system, and it is projected onto a low dimensional space based on principal component analysis. The low dimensional motion pattern is modified, so that the high dimensional motion pattern satisfies the motion conditions, dynamical consistency and joint angle and torque limitations. The proposed method is applied to the motion pattern design of the planar bipedal robot. The moon-walk performed by a human is transformed to the robot motion. In this case, the motion conditions are the kinematic closed loop condition and the ground contact states of foot links. The floor reaction force condition and the satisfaction of the motion equation are given for dynamical consistency.