The McKibben artificial muscle is a pneumatic soft actuator consisting of a rubber tube, with fibers covering it. In this study, we propose a new fabrication process to easily realize twisting artificial muscles by utilizing braiding technology and water-soluble fibers based on the McKibben artificial muscle. In the braiding process, half of the sleeve fibers of the artificial muscle are substituted with water-soluble fibers. However, the water-soluble fibers are removed by placing the muscle in warm water to realize a twisting motion. By changing the direction of the fibers, artificial muscles that twist clockwise or counterclockwise can be produced. We believe that the establishment of such a simple and efficient fabrication process will promote to the practical application of artificial muscles.

A McKibben artificial muscle is a fluid-driven soft actuator comprising sleeve fibres and rubber tube. However, as typical bulky and rigid displacement sensors are unsuitable as sensor elements in soft actuators, displacement sensing is challenging for the McKibben artificial muscle. Therefore, we propose an optical fibre-based smart artificial muscle (OSAM) to estimate self-displacement from the bending loss of the optical fibre used as the sleeve fibre. The optical fibre can be effortlessly integrated into the OSAM sleeve using a braiding machine, which is generally used for manufacturing strings, easing the mass production process. The radius of curvature of the optical fibre changed when the OSAM was driven. The displacement of the artificial muscle was estimated based on the sensor output. To demonstrate the usefulness of OSAM, displacement feedback control experiments were conducted using the optical fibre sensor integrated into OSAM. From the results, OSAM’s displacement showed a good response to the target displacement. Therefore, the developed artificial muscle can facilitate displacement feedback control without requiring external sensors, which in turn can improve the performance of rehabilitation and wearable devices.

A McKibben artificial muscle is a soft actuator driven by air pressure, characterized by its flexibility, lightweight design, and high power-to-weight ratio. We have developed a smart artificial muscle that is capable of sensing its motion. To enable this sensing function, an optical fiber was integrated into the sleeve consisting of multiple fibers and serving as a component of the McKibben artificial muscle. By measuring the macrobending loss of the optical fiber, the length of the smart artificial muscle is expected to be estimated. However, experimental results indicated that the sensor’s characteristics depend not only on the length but also on the load and the applied air pressure. This dependency arises because the stress applied to the optical fiber increases, causing microbending loss. In this study, we employed a machine learning model, primarily composed of Long Short-Term Memory (LSTM) neural networks, to estimate the length of the smart artificial muscle. The experimental results demonstrate that the length estimation obtained through machine learning exhibits a smaller error. This suggests that machine learning is a feasible approach to enhancing the length measurement accuracy of the smart artificial muscle.

A compact axial active magnetic bearing with a function of two-tilt-motion control is fabricated which has a new configuration of magnetic poles. They consist of four cylindrical poles with coils and a single common pole whose opposite plane of the rotor has a permanent magnet to achieve multi-degree-of-freedom zero power control. Modal control is applied because local zero power control may make the whole system unstable when the number of control channels is larger than the number of freedoms of motion to be controlled. In the developed system, a disk-shape rotor is sandwiched between two axial magnetic bearing stators that are operated differentially. Such a configuration makes it possible to rotate the rotor without disturbing the axial motion. The characteristics of the fabricated magnetic bearing system including rotation are studied experimentally.

A thin McKibben artificial muscle is a pneumatic actuator with an outer diameter of only 1.8 mm. We fabricated a string-shaped actuator called an “active string actuator,” which achieves a high contractile displacement by accumulating thin McKibben artificial muscles. To control the displacement, the length of the active string actuator should be estimated. However, this is difficult because bulky and rigid sensors are unsuitable for the sensor element of the active string actuator. Therefore, in this study, we propose a new sensing method for estimating the length of an active string actuator. The proposed sensing system is simple and comprises only three components: a step-index multimode optical fiber, a light emitter, and a light receiver. A step-index multimode optical fiber was combined with the active string actuator, and the length was estimated from the change in the amount of light propagating in the optical fiber when the active string actuator was driven. Fundamental experiments were conducted in this study, and the results demonstrated that the optical fiber sensor value changed with the actuator length. This suggests that it is possible to estimate the displacement of an active string actuator using an optical fiber sensor.

In our study, a soft robot arm consisting of McKibben artificial muscles and a silicone rubber structure was developed. This robot arm can perform bending and twisting motions by ap-plying pneumatic pressure to the artificial muscles. The robot arm is made of flexible materials only, and therefore it has high flexibility and shape adaptability. In this report on the fundamental investigation of the master–slave feedback control of the soft robot arm for intentional operation, we focus on the bending motion of the soft robot arm. Three flexible strain sensors were placed on the soft robot arm for measuring the bending motion. By establishing a master–slave feedback system using the sensors, the bending motion of the soft robot arm followed the operator’s wrist motion detected via the wearable interface device.