An octopus arm with a flexible structure and no rigid skeleton shows a high degree of freedom and flexibility. These excellent features are suitable for working in an environment having fragile and unknown-shaped objects. Therefore, a soft robot arm resembling an octopus arm can be useful as a harvesting machine without damaging crops in the agricultural field, as a rehabilitation apparatus in the welfare field, as a safe surgery tool in the medical field, and so on. Unlike industrial robots, to consider the applications of the soft robot arm, the instructions for it relating to a task cannot in many cases be given as a numerical value, and the motion according to an operator’s sense and intent is useful. This paper describes the design and feedback control of a soft master-slave robot system. The system is configured with two soft rubber machines; one is a slave machine that is the soft robot arm mimicking the muscle arrangement of the octopus arm by pneumatic artificial muscles, and the other is a master machine that gives the target motion to the slave machine. Both are configured with soft materials. The slave machine has an actuating part and a sensing part, it can perform bending and torsional motions, and these motions are estimated by the sensing part with threads that connect to wire encoders. The master machine is almost the same configuration, but it has no actuating part. The slave machine is driven according to the deformation of the master machine. We confirmed experimentally that the slave machine followed the master machine that was deformed by an operator.

In the development of a microrobot for inspecting the inside of pipes with an internal diameter of 1 to 3 inches, the author of this paper designed an in-pipe mobile mechanism adaptive to pipe diameter based on a snake robot and evaluated it theoretically and experimentally. The authors fabricated 2 robots of different size. The first, Prototype A, was used to verify functions and confirm the driving mechanism. It demonstrated basic motion and moved in all directions during an experiment in which the robot passed through a T-shaped pipe. The second, Prototype B, was downsized and could run in narrow channel whose width changes from 0.7 to 3.9 inches.

This manuscript explains the employment of flexible actuators to act as a soft robot and transporting agent to assist medical X-ray examinations. Although soft robots from silicone material can be transparence and a human compliance used as medical assistive devices, soft robots have some problems: they tend to be sluggish, have long and imprecise gait trajectories, and need their control parameters to be adjusted for motion diversion. A soft robot with omnidirectional locomotion has been created, one that has a combination of pneumatic rubber legs that form a soft robot platform and an associated hardware setup. Tests have confirmed its omnidirectional locomotion ability; it has a maximum speed of 6.90 mm/s in forward locomotion and a maximum payload of 70 g. These features indicate that the robot can be used as a medical assistive device for fluoroscopy examinations.

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.