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This study aims to discuss the mathematical modelling of a compliance-assisted flapping mechanism and morphable structures for an UAV.

A compliance-assisted flapping wing was designed and modelled mathematically, and signals for the corresponding curves were calculated. The actual wing tip trace of a hummingbird was taken, and variables a, b, h and k were calculated from the image. This data was given to the mathematical model for plotting the graph, and the curve was compared with the input curve. The wing frame and mechanism for control surfaces using morphing is modelled along with single pivoted spine for centre of gravity augmentation and flight orientation control.

The model efficiently approximates the 2D path of the wing using line segments using the muscle and compliance mechanism.

Using a compliance-assisted flapping mechanism offers practical advantages. It allows us to synchronize the flapping frequency with the input signal frequency, ensuring efficient operation. Additionally, the authors can enhance the torque output by using multiple muscle strands, resulting in a substantial increase in the system’s torque-to-weight ratio. This approach proves to be more favourable when compared to conventional methods involving motors or servos, ultimately offering a more efficient and robust solution for practical application.

This model focuses on creating a flexible and tunable mechanism that can at least trace four types of wing traces from the same design, for shifting from one mode of flight to another.

Conventional ornithopter flapping mechanisms are gear or servo driven and cannot trace a wing tip, but some can trace complicated curves, but only one at a time. This model can trace multiple curves using the same hardware, allowing the user to program the curve based on their needs or bird. The authors may vary the shape of the wing tip trace to switch between forward flight, hovering, backward flying, etc., which is not conceivable with any traditional flapping mechanism.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

The purpose of this study is realizing human-like motions and performance through musculoskeletal robots and brain-inspired controllers. Human-inspired robotic systems, owing to their potential advantages in terms of flexibility, robustness and generality, have been widely recognized as a promising direction of next-generation robots.

In this paper, a deep forward neural network (DFNN) controller was proposed inspired by the neural mechanisms of equilibrium-point hypothesis (EPH) and musculoskeletal dynamics.

First, the neural mechanism of EPH in human was analyzed, providing the basis for the control scheme of the proposed method. Second, the effectiveness of proposed method was verified by demonstrating that equilibrium states can be reached under the constant activation signals. Finally, the performance was quantified according to the experimental results.

Based on the neural mechanism of EPH, a DFNN was crafted to simulate the process of activation signal generation in human motion control. Subsequently, a bio-inspired musculoskeletal robotic system was designed, and the high-precision target-reaching tasks were realized in human manner. The proposed methods provide a direction to realize the human-like motion in musculoskeletal robots.

This study aims to conduct text mining and semantic network analysis of muscle-supportive and posture-corrective wearable robots for the elderly to understand key terms related to the topic and to identify considerations for developing these types of clothing.

The authors searched and identified the key terms wearable robot, muscle-supportive, posture correction and elderly using the text-mining software Textom to extract terms as well as the network analysis software UCINET 6 to process and visualize the relationships among the terms. The authors compared and analyzed the term frequency (TF), the TF-inverse document frequency and the degree centrality of the terms, and the authors visualized and summarized the terms using NetDraw.

The key terms and their relationships in 3–4 groups were identified: wearable robot, muscle-supportive, posture correction and elderly. The authors identified the aspects of designing muscle-supportive and posture-corrective wearable robots for the elderly.

This study contributes to the field of muscle-supportive clothing and wearable robotics by deriving insights into what people are discussing and interested in, and by offering recommendations when developing these types of clothing for the elderly.

This study discusses the tracking trajectory issue of the exoskeleton under the bounded disturbance and designs an useful tracking trajectory control method to solve it. By using the proposed control method, the tracking error can be successfully convergence to the assigned boundary. Meanwhile, the chattering effect caused by the actuators is already reduced, and the tracking performance of the pneumatic artificial muscles (PAMs) elbow exoskeleton is improved effectively.

A prescribed performance sliding mode control method was developed in this study to fulfill the joint position tracking trajectory task on the elbow exoskeleton driven by two PAMs. In terms of the control structure, a dynamic model was built by conforming to the adaptive law to compensate for the time variety and uncertainty exhibited by the system. Subsequently, a super-twisting algorithm-based second-order sliding mode control method was subjected to the exoskeleton under the boundedness of external disturbance. Moreover, the prescribed performance control method exhibits a smooth prescribed function with an error transformation function to ensure the tracking error can be finally convergent to the pre-designed requirement.

From the theoretical perspective, the stability of the control method was verified through Lyapunov synthesis. On that basis, the tracking performance of the proposed control method was confirmed through the simulation and the manikin model experiment.

As revealed by the results of this study, the proposed control method sufficiently applies to the PAMs elbow exoskeleton for tracking trajectory, which means it has potential application in the actual robot-assisted passive rehabilitation tasks.

The purpose of this study is to explore the synthesis of novel conductive photo-resins to produce flexible conducting composites for use in additive manufacturing. By using direct ink writing (DIW) additive manufacturing, this study aims to explore the fabrication of multimaterial devices with conductive and insulating components. Using digital light processing (DLP) additive manufacturing, this study aims to fabricate detailed objects with higher resolution than material extrusion 3D printing systems.

In this paper, several photocurable conducting resins were prepared for DIW and DLP additive manufacturing. These resins were then cured using 405 nm near UV light to create intrinsically conductive polymer (ICP) composites. The electrochemical properties of these composites were analysed, and the effect of co-monomer choice and crosslinking density was determined. These results determined a suitable resin for subsequent additive manufacture using DIW and DLP. These 3D printing techniques were used to develop flexible conducting devices of submillimetre resolution that were fabricated with unmodified, commercially available 3D printers.

Cyclic voltammetry and volume conductivity analysis of the conducting resins determined the most conductive resin formula for 3D printing. Conductive devices were fabricated using the two 3D printing techniques. A multimaterial soft conducting device was fabricated using DIW, and each conducting component was insulated from its neighbours. DLP was used to fabricate a soft conducting device with good XY resolution with a minimum feature size of 0.2 mm. All devices were prepared in unmodified commercially available 3D printers.

These findings have value in the development of soft robotics, artificial muscles and wearable sensors. In addition, this work highlights techniques for DIW and DLP additive manufacturing.

Several original conducting resin formulae were developed for use in two 3D printing systems. The resulting 3D-printed composites are soft and flexible while maintaining their conductive properties. These findings are of value to both polymer chemists and to the field of additive manufacturing.

An increasing aging population and an increasing number of people suffering from musculoskeletal disorders have increased the demand for wearable robots. Comfortable, wearable robots that can be worn like clothing are currently being investigated. However, the embedded components may be displaced owing to the flexibility of the fabrics, which can lower the sensing accuracy and limit natural body movements. This study aims to develop clothing-type wearable platforms to minimize the displacement of embedded components such as sensors and actuators while maintaining comfort.

Four designs were developed using materials with different seam lines, that can serve as anchoring details, and flatlock stitches considering body movements and musculoskeletal structures. The wear evaluation experiment was filmed using a speed camera and analyzed using the TimeViewer software and SPSS 26.0. Based on these results, four clothing-type wearable platform designs were developed.

The variation in the location of a point in the armhole among the designs was marginal. Participants were satisfied with the functionality, practicality, wearability, efficiency and ease of use of the developed designs. A final clothing-type wearable platform was developed by applying a design with the least change in location, a suitable design for each area and wear comfort.

The results of this study contribute to the development of wearable robots by establishing clothing design data to minimize changes in sensor and actuator movements.

This paper aims to propose a novel system design and control algorithm of lower limb exoskeleton, which provides walking assistance and load sharing for the wearer.

In this paper, the valve-controlled asymmetrical hydraulic cylinder is selected for driving the hip and knee joint of exoskeleton. Pressure shoe is developed that purpose on detecting changes in plantar force, and a fuzzy recognition algorithm using plantar pressure is proposed. Dynamic model of the exoskeleton is established, and the sliding mode control is developed to implement the position tracking of exoskeleton. A series of prototype experiments including benchtop test, full assistance, partial assistance and loaded walking experiments are set up to verify the tracking performance and power-assisted effect of the proposed exoskeleton.

The control performance of PID control and sliding mode control are compared. The experimental data shows the tracking trajectories and tracking errors of sliding mode control and demonstrate its good robustness to nonlinearities. sEMG of the gastrocnemius muscle tends to be significantly weakened during assisted walking.

In this paper, a structure that the knee joint and hip joint driven by the valve-controlled asymmetrical cylinder is used to provide walking assistance for the wearer. The sliding mode control is proposed to deal with the nonlinearities during joint rotation and fluids. It shows great robustness and frequency adaptability through experiments under different motion frequencies and assistance modes. The design and control method of exoskeleton is a good attempt, which takes positive impacts on the productivity or quality of the life of wearers.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

The purpose of this paper is to study the mechanochemical effect and self-growth mechanism of double-network (DN) gel and to provide a quasiperiodic model for rubber elasticity.

The chemical reaction kinetics is used to identify the mechanochemical transition probability of host brittle network and to explore the mechanical behavior of endosymbiont ductile network. A quasiperiodic model is proposed to characterize the cooperative coupling of host–endosymbiont networks using the Penrose tiling of a 2 × 2 matrix. Moreover, a free-energy model is formulated to explore the constitutive stress–strain relationship for the DN gel based on the rubber elasticity theory and Gent model.

In this study, a quasiperiodic graph model has been developed to describe the cooperative interaction between brittle and ductile networks, which undergo the mechanochemical coupling and mechanical stretching behaviors, respectively. The quasiperiodic Penrose tiling determines the mechanochemistry and self-growth effect of DNs.

It is expected to formulate a quasiperiodic graph model of host–guest interaction between two networks to explore the working principle of mechanical and self-growing behavior in DN hydrogels, undergoing complex mechanochemical effect. The effectiveness of the proposed model is verified using both finite element analysis and experimental results of DN gels reported in literature.