Hiroyuki Nabae

McKibben Artificial Muscles (AMs) are known as a prominent class of pneumatic actuators in soft robotics and biomechanical engineering, due to their unique structure and multifunctionality. This paper presents a comprehensive review of recent advancements in McKibben AMs, focusing on their performance, structural variations, and operational principles. A systematic literature search on Scopus identified 146 relevant articles, which were analyzed for both performance metrics and design characteristics. Inspired by natural muscle behavior, McKibben AMs enable complex motions such as bending, linear extension, and twisting. These actuators can be organized as individual or bundled systems: individual units are typically arranged in linear or circular patterns, while bundled systems occur in serial, parallel, braided, convergent, or pennate configurations. Recent innovations in smart actuation methods, braided sleeves and internal bladders have expanded their capabilities, enabling embedded sensing, environmental adaptability and untethered operation. Additionally, alternative manufacturing methods offer promising solutions for developing McKibben muscles with enhanced functionality and tailored properties.

Biomimetics replicates biological functions and helps investigate mechanisms and movements. This study focuses on mimicking canine limb structures, particularly the flexible forelimb connection. Due to vestigial clavicles, dogs’ forelimbs attach to the trunk via muscles, creating a suspension system, unlike the skeletal hip joint connection of hindlimbs. Zoological studies highlight this structure's role in movement, but robotics has largely overlooked it. This research aims to clarify its function in locomotion by constructing a musculoskeletal canine‐limb robot using thin McKibben muscles, known for their lightweight, flexibility, and contraction ability even when bent. Experiments demonstrated that adjusting pneumatic pressure in the shoulder muscles influenced stroke length, confirming that the hammock‐like structure enhances movement. Increased pressure resulted in longer strokes, contributing to efficient locomotion. Walking experiments verified successful straight‐line progression using a walking gait, demonstrating the feasibility of this biomimetic approach. These findings suggest that incorporating flexible shoulder structures in quadrupedal robots could improve mobility and adaptability. Future research could further optimize muscle control and gait patterns to enhance robotic performance. By better understanding and implementing biological musculoskeletal features, this study contributes to both zoological biomechanics and robotic advancements, bridging the gap between biology and engineering for more efficient locomotion.

Soft robotics requires compliant actuators for safe human interaction. While McKibben artificial muscles are popular for their high force output, their reliance on bulky, noisy pumps limits their use in wearable devices. Electrohydrodynamic (EHD) pumps offer a compact and silent alternative, but existing designs struggle with dielectric discharge and fabrication issues, which compromise reliability and power density. This study introduces a novel EHD pump featuring 0.1 mm copper wire electrodes in a diagonal arrangement within a laser‐cut acrylic frame. This design improves dielectric resilience, minimizes deformation, and allows for compact integration. A new simplified fabrication process results in sample variation under 5%. The pump demonstrates remarkable performance, achieving 107 kPa pressure and an 88 mL min ⁻¹ flowrate, doubling the power density of the previous model while retaining 88% of its flowrate after 50 discharge events. An automated self‐recovery mechanism is also implemented, enabling the pump to instantly restore function after a discharge. When paired with a McKibben muscle, the system achieves a 2 s contraction time, a tenfold improvement over the prior EHD‐driven system. This work presents a significant advancement in fast, resilient, and scalable actuation, paving the way for next‐generation wearable robotics and assistive technologies.

Despite rapid advances in soft robotics for biomedical applications, little attention has been paid to its potential for simulating interactive nursing procedures that involve complex human responses. In this study, the first soft robotic simulator was specifically designed for training in the digital removal of feces (DRF)—a delicate, discomfort‐sensitive nursing technique frequently performed in home care settings in Japan. Existing DRF training relies on static models that fail to replicate the physiological responses of patients, often leading to inadequate skill acquisition and patient discomfort. To address this gap, we implement a novel simulator incorporating two soft actuators (a pouch‐type actuator for anal sphincter contraction and a McKibben muscle for puborectalis relaxation) and two soft sensors for detecting anal and rectal massage. These are embedded into a multilayer rectal model and integrated with a real‐time feedback interface. Our design uniquely prioritizes both functional fidelity and perceptual realism, enabling trainees to learn from physiological reactions that mimic actual patient responses. Validation with experienced and novice nurses confirms the simulator's effectiveness in improving procedural understanding and confidence. This study demonstrates a new application of soft robotics—not only for anatomical replication, but also as a practical training tool for hands‐on nursing procedures.

Flexible/stretchable sensors comprising soft structures that do not interfere with the softness of the bodies of soft robots are essential for achieving soft robots with superior operational performance. These sensors are expected to be applicable as sensing skin for humanoid robots and as interfaces for human-robot interactions. Herein, we refer to flexible/stretchable sensors investigated for soft robotics applications as “soft sensors” and review recent research trends. Specifically, we discuss optical, resistive, capacitive, and inductive soft sensors with emphasis on their materials and structures.

The McKibben muscle, widely employed in antagonistic drive robots, exhibits approximately 20% contraction under pneumatic pressure but lacks crucial passive extensibility. Previous attempts to achieve passive extensibility resulted in reduced overall contraction ratios due to the series connection of elongation and contraction sections. This study proposes a novel artificial muscle design that achieves extension through external force by arranging extensible and contractile components in parallel. The proposed design incorporates a composite thread consisting of an elastic thread and an inextensible thread connected in series, arranged parallel to a conventional McKibben muscle. This configuration yields an artificial muscle capable of approximately 16% contraction and over 40% extension under external force when relaxed. The simplicity of this design facilitates straightforward integration into robotic systems. To demonstrate the efficacy of the proposed artificial muscle, we applied it to a tensegrity robot, exemplifying an antagonistic drive system. This application showcases the potential of our design to enhance the performance and versatility of soft robotic systems.

Methods utilizing mechanical structures or chemical reactions to simplify complex control systems (such as solenoid valves required for pneumatically driven soft actuators) have been gaining interest. However, movements developed from these approaches are limited to the combined abilities of the robot's components. That is to say, adapting a robot's motion to its environment carries a high developmental and computational cost. Here, we focus on self-excited vibration to achieve a robot that can generate adaptive behavior through interaction with its environment. Specifically, we propose a serially coupled self-excited pneumatic actuator that can change its motion in response to contact with surrounding structures. The actuator is modeled and prototyped to evaluate its performance. Subsequently, we apply this actuator to a robot that can steer itself without a complex control system. We experimentally confirm that the robot can execute basic trajectories, including corners.

A tensegrity-based system is a promising approach for dynamic exploration of uneven, unpredictable, and confined environments. However, implementing such systems presents challenges in state recognition. In this study, we introduce a 6-strut tensegrity structure integrated with 24 multimodal strain sensors, employing a deep learning model to achieve smart tensegrity. By using conductive flexible tendons and leveraging a long short-term memory (LSTM) model, the system accomplishes self-shape reconstruction without the need for external sensors. The sensors operate in two modes, and we applied both a curve fitting model and an LSTM model to establish the relationship between length change and resistance change in the sensors. Our key findings demonstrate that the intelligent tensegrity system can accurately self-detect and adapt its shape. Furthermore, a human pressing process allows users to monitor and understand the tensegrity's shape changes based on the integrated models. This intelligent tensegrity-based system with self-sensing tendons showcases significant potential for future exploration, making it a versatile tool for real-world applications.

The McKibben muscle can produce a high force-to-mass ratio, beneficial for various applications in the soft mechatronics field. The thin McKibben muscle, which has a small diameter, has the advantage of a high force-to-mass ratio and sufficient flexibility for use in a bent state. This flexibility permits the realization of flexible mechatronics. However, the thin McKibben muscle is easily broken in a bent state while it is very durable in a straight state. Over repetitive operations, the fibers within the sleeve gradually shift, causing the rubber tube inside to protrude and ultimately leading to cracking. This study investigates improvements in the durability of artificial muscles using adhesives to prevent this fiber-to-fiber misalignment. The durability test showed that the adhesive could provide a durability of up to 10,000-times greater than that of a normal thin artificial muscle in the maximum case. Using the thin McKibben muscle with the proposed method, tensegrity modules were fabricated. The durability test revealed a 500-fold increase under an applied pressure of 0.5 MPa. Furthermore, the durability of the adhesive-applied artificial muscles was also confirmed to be enhanced during the dynamic movements of a soft tensegrity robot that throws a ball with 0.7 MPa.