Search Results (190)

To enable the practical application of passive back-support exoskeletons employing pneumatic artificial muscles (PAMs) in tasks such as agricultural work, we evaluated their assistive effectiveness in a half-squatting posture with a staggered stance. In this context, assistive force profiles were adjusted according to body posture to achieve more effective support. The targeted assistive force profile was designed to be continuously active from the standing to the half-squatting position, with minimal variation across this range. The assistive force profile was developed based on a PAM contractile force model and implemented using a cam mechanism. The effectiveness of assistance was assessed by measuring body flexion angles and erector spinae muscle activity during lifting and carrying tasks. The results showed that the assistive effect was greater on the side with the forward leg. Compared to the condition without exoskeleton assistance, the conventional pulley-based system reduced muscle activity by approximately 20% whereas the cam-based system achieved a reduction of approximately 30%. Full article

Pneumatic artificial muscles (PAMs) consist of an elastomeric bladder wrapped in a Kevlar braid. When inflated, PAMs expand radially and contract axially, producing large axial forces. PAMs are often utilized for their high specific work and specific power, as well as their ability to produce large axial displacements. Although the axial behavior of PAMs is well studied, the radial behavior has remained underutilized and is poorly understood. Modeling was performed using a force balance approach to capture the effects that bladder strain and applied axial load have on the anchoring force. Radial expansion testing was performed to validate the model. Force due to anchoring was recorded using force transducers attached to sections of aluminum pipe using an MTS servo-hydraulic testing machine. Data from the test were compared to the predicted anchoring force. Radial expansion in large-diameter (over 50.8 mm) PAMs was then used in worm-like robots to create anchoring forces that allow for a peristaltic wave, which creates locomotion through acrylic pipes. By radially expanding, the PAM presses itself into the pipe, creating an anchor point. The previously anchored PAM then deflates, which propels the robot forward. Modeling of the radial expansion forces and anchoring was necessary to determine the pressurization required for proper anchoring before slipping occurs due to the combined robot and payload weight. Full article

Soft-growing robots based on the eversion principle are renowned for their ability to rapidly extend along their longitudinal axis, allowing them to access remote, confined, or otherwise inaccessible spaces. Their inherently compliant structure enables safe interaction with delicate environments, while their simple actuation mechanisms support lightweight and low-cost designs. Despite these benefits, implementing effective navigation mechanisms remains a significant challenge. Previous research has explored the use of pneumatic artificial muscles mounted externally on the robot’s body, which, when contracting, induce directional bending. However, this method only offers limited bending performance. To enhance maneuverability, pneumatic artificial muscles embedded in between the walls of double-walled eversion robots have also been considered and shown to offer superior bending performance and force output as compared to externally attached muscle. However, their adoption has been hindered by the complexity of the current manufacturing techniques, which require individually sealing the artificial muscles. To overcome this multi-stage fabrication approach in which muscles are embedded one by one, we propose a novel single-step method. The key to our approach is the use of non-heat-sealable inserts to form air channels during the sealing process. This significantly simplifies the process, reducing production time and effort and improving scalability for manufacturing, potentially enabling mass production. We evaluate the fabrication speed and bending performance of robots produced in this manner and benchmark them against those described in the literature. The results demonstrate that our technique offers high bending performance and significantly improves the manufacturing efficiency. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

Adaptive vehicle control systems are crucial for enhancing safety, performance, and efficiency in modern transportation, particularly as vehicles become increasingly automated and responsive to dynamic environments. This review explores the advancements in bio-inspired actuators and their potential applications in adaptive vehicle control systems. Bio-inspired actuators, which mimic natural mechanisms such as muscle movement and plant tropism, offer unique advantages, including flexibility, adaptability, and energy efficiency. This paper categorizes these actuators based on their mechanisms, focusing on shape memory alloys, dielectric elastomers, ionic polymer–metal composites, polyvinylidene fluoride-based electrostrictive actuators, and soft pneumatic actuators. The review highlights the properties, operating principles, and potential applications for each mechanism in automotive systems. Additionally, it investigates the current uses of these actuators in adaptive suspension, active steering, braking systems, and human–machine interfaces for autonomous vehicles. The review further outlines the advantages of bio-inspired actuators, including their energy efficiency and adaptability to road conditions, while addressing key challenges like material limitations, response times, and integration with existing automotive control systems. Finally, this paper discusses future directions, including the integration of bio-inspired actuators with machine learning and advancements in material science, to enable more efficient and responsive adaptive vehicle control systems. Full article

Simulating low- or zero-gravity environments on the ground is an important technology in the field of space exploration. Suspension-based gravity offloading (SGO) systems are commonly used ground-based platforms for simulating space environments. Nevertheless, the control of SGO systems presents significant challenges due to external disturbances and stringent response time requirements. This study proposes a predefined-time (PdT) observer-based PdT control framework to address the SGO system control problem. To begin, a pneumatic artificial muscle-based active unloading mechanism is introduced to address the inherent underactuation problem in the SGO system. Thereafter, a PdT disturbance observer is introduced to estimate the external disturbances acting on the SGO system. Subsequently, a type of PdT controller is investigated for the SGO system. The system, including the PdT disturbance observer and the PdT controller, is proven to be PdT stable in the Lyapunov sense. Finally, sufficient numerical simulations and physical experiments are conducted to verify the superiority and effectiveness of the proposed control method. Full article

Industrial workers often engage in repetitive lifting tasks. This type of continual loading on their arms throughout the workday can lead to muscle or tendon injuries. A non-intrusive system designed to assist a worker’s arms would help alleviate strain on their muscles, thereby preventing injury and minimizing productivity losses. The goal of this project is to develop a wearable soft robotic arm enhancement device that supports a worker’s muscles by sharing the load during lifting tasks, thereby increasing their lifting capacity, reducing fatigue, and improving their endurance to help prevent injury. The device should be easy to use and wear, functioning in relative harmony with the user’s own muscles. It should not restrict the user’s range of motion or flexibility. The human arm consists of numerous muscles that work together to enable its movement. However, as a proof of concept, this project focuses on developing a prototype to enhance the biceps brachii muscle, the primary muscle involved in pulling movements during lifting. Key components of the prototype include a soft robotic muscle or actuator analogous to the biceps, a control system for the pneumatic muscle actuator, and a method for securing the soft muscle to the user’s arm. The McKibben-inspired pneumatic muscle was chosen as the soft actuator for the prototype. A hybrid control algorithm, incorporating PID and model-based control methods, was developed. Electromyography (EMG) and pressure sensors were utilized as inputs for the control algorithms. This paper discusses the design strategies for the device and the preliminary results of the feasibility testing. Based on the results, a wearable EMG-controlled soft robotic arm augmentation could effectively enhance the endurance of industrial workers engaged in repetitive lifting tasks. Full article

This paper reviews the research progress of bionic soft robot technology learned from octopuses. The number of related research papers increased from 760 in 2021 to 1170 in 2024 (Google Scholar query), with a growth rate of 53.95% in the past five years. These studies mainly explore how humans can learn from the physiological characteristics of octopuses for sensor design, actuator development, processor architecture optimization, and intelligent optimization algorithms. The tentacle structure and nervous system of octopus have high flexibility and distributed control capabilities, which is an important reference for the design of soft robots. In terms of sensor technology, flexible strain sensors and suction cup sensors inspired by octopuses achieve accurate environmental perception and interaction. Actuator design uses octopus muscle fibers and movement patterns to develop various driving methods, including pneumatic, hydraulic and electric systems, which greatly improves the robot’s motion performance. In addition, the distributed nervous system of octopuses inspires multi-processor architecture and intelligent optimization algorithms. This paper also introduces the concept of expected functional safety for the first time to explore the safe design of soft robots in failure or unknown situations. Currently, there are more and more bionic soft robot technologies that draw on octopuses, and their application areas are constantly expanding. In the future, with further research on the physiological characteristics of octopuses and the integration of artificial intelligence and materials science, octopus soft robots are expected to show greater potential in adapting to complex environments, human–computer interaction, and medical applications. Full article

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. Full article

This paper presents a model predictive controller (MPC) based on dynamic models generated using the Particle Swarm Optimisation method for accurate motion control of a pneumatic artificial muscle (PAM) for application in rehabilitation robotics. The physical compliance and lightweight nature of PAMs make them desirable for use in the field but also introduce nonlinear dynamic properties which are difficult to accurately model and control. As well as the MPC, three other control systems were examined for a comparative study: a particle-swarm optimised proportional-integral-derivative controller (PSO-PID), an iterative learning controller (ILC), and classical PID control. A series of different waveforms were used as setpoints for each controller, including addition of external loading and simulated disturbance, for a system consisting of a single PAM. Based on the displacement error measured for each experiment, the PID controller performed worst with the largest error values and an issue with oscillating about the setpoint. PSO-PID performed better but still poorly compared with the other intelligent controllers, as well as still exhibiting oscillation, which is undesirable in any human–robot interaction as it can heavily impact the comfort and safety of the system. ILC performed well with rapid convergence to steady-state and low-error values, as well as mitigation of loads and disturbance; however, it performed poorly under changing frequency of input. MPC generally performed the best of the controllers tested here, with the lowest error values and a rapid response to changes in setpoint, as well as no required learning period due to the predictive algorithm. Full article

McKibben’s muscle (MKM) is the most adopted among the different types of pneumatic artificial muscles (PAMs) due to its mechanical performance and versatility. Several geometric parameters, including the diameter, thickness, and length of the inner elastic element, as well as functional conditions, such as shortening ratio and feeding pressure, influence the behaviour of this actuator. Over the years, analytical and numerical models have been defined to predict its deformation and developed forces. However, these models are often identified under simplifications and have limitations when integrating new parameters that were not initially considered. This work proposes a hybrid approach between finite element analyses (FEAs) and machine learning (ML) algorithms to overcome these issues. An MKM was numerically simulated as the chosen parameters changed, realizing the MKM dataset. The latter was used to train 27 artificial neural networks (ANNs) to identify the best algorithm for predicting the developed forces. The best ANN was tested on three numerical models and a prototype with a combination of parameters not included in the dataset, comparing predicted and numerical responses. The results demonstrate the effectiveness of ML techniques in predicting the behavior of MKMs while offering flexibility for integrating additional parameters. Therefore, this paper highlights the potential of ML approaches in the mechanical design of MKM according to the field of use and application. Full article

Strength training machines incorporating advanced electro-mechanical technologies can produce hybrid resistances with variable inertia, such as a resistance that progressively changes from gravitational (inertial) to pneumatic (non-inertial) across the range of motion (ROM). To explore the biomechanical effects of these innovative resistances, a robotic chest press machine was programmed to offer three distinct inertial profiles: gravitational-type constant inertia throughout the ROM (I_FULL); no inertia (I_ZERO); and linearly descending inertia across the ROM (I_VAR). Ten healthy adults performed five maximal-effort, explosive chest press movements under each inertial profile at 30, 50 and 70% of their one-repetition maximum. During each trial, muscle activity of the pectoralis major, anterior deltoid, and triceps brachii was recorded, along with force, velocity and power outputs from the machine. Statistical non-parametric maps based on two-way repeated measures ANOVA were used to assess the effects of load level and inertial profile on the collected time series. Higher load levels consistently led to increased force and reduced velocity and power outcomes over large parts of the ROM. Compared to I_FULL, I_ZERO allowed for greater velocity at the expense of lower force throughout the ROM, while I_VAR produced higher force and power outputs despite having lower velocity than I_ZERO. Additionally, both I_ZERO and I_VAR significantly increased triceps brachii activity at the end of the ROM compared to I_FULL. I_VAR outperformed both I_FULL and I_ZERO in terms of force and power. Coaches and therapists are advised to consider variable inertial profiles as a key parameter when designing exercise programs for athletes or patients. Full article

This study explores the integration of a custom-designed pneumatic spring into a car-seat cushion and its interaction with a simplified human body model using the Finite Element Method (FEM). A 3D half-symmetry FEM framework, developed from experimental data, ensured computational efficiency and convergence. This research bridged experimental and numerical approaches by analyzing the contact pressure distributions between a seat cushion and a volunteer with representative biometric characteristics. The model incorporated two material groups: (1) human body components (bones and muscles) and (2) seat cushion materials (polyurethane foam, latex, and fabric tape). Mechanical properties were obtained from both the literature and experiments, and simulations were conducted using MSC.Marc software under realistic boundary and initial conditions. The simulation results exhibited strong agreement with experimental data, validating the model’s reliability in predicting contact pressure distribution and optimizing seat cushion designs. Contrary to the conventional notion that uniformly distributed contact pressure inherently enhances comfort, this study emphasizes that the precise localization of pressure plays a crucial role in static and long-term seating ergonomics. Both experimental and simulation results demonstrated that modulating the pneumatic spring’s internal pressure from 0 kPa to 25 kPa altered peak contact pressure by approximately 3.5 kPa (around 20%), significantly influencing pressure redistribution and mitigating high-pressure zones. By validating this FEM-based approach, this study reduces dependence on physical prototyping, lowering design costs, and accelerating the development of ergonomically optimized seating solutions. The findings contribute to a deeper understanding of human–seat interactions, offering a foundation for next-generation automotive seating innovations that enhance comfort, fatigue reduction, and adaptive pressure control. Full article

Pneumatic artificial muscles (PAMs) consist of an elastomeric bladder wrapped in a helical braid. When inflated, PAMs expand radially and contract axially, producing large axial forces. PAMs are advantageous because of their high specific work and specific power, as well as their ability to produce large axial displacements. The axial and radial behavior of PAMs have been well studied. The torsional response of PAMs have not been explored before. Accurate prediction of the torsional force was desired for use in a bio-inspired worm-like robot capable of using an auger mounted to a PAM to bore out tunnels. Thus, an understanding of torsional response was a key objective. Modeling of the torsional response was performed using a force balance approach, and multiple model variations were considered, such as St. Venant’s torsion, bladder buckling, and asymmetrical braid loading. Torsional testing was performed to validate the model using a custom torsional testing system. Data from the tests was compared to the predicted torsional response. Full article