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The purpose of this paper is to propose a casting process for the production of double-chamber soft fingers, which avoids the problems of air leakage and fracture caused by multistep casting. This proposed method facilitates the simultaneous casting of the inflation chamber and the jamming chamber.

An integrated molding technology based on the lost wax casting method is proposed for the manufacture of double-chamber soft fingers. The solid wax core is assembled with the mold, and then liquid silicone rubber is injected into it. After cooling and solidification, the mold is stripped off and heated in boiling water, so that the solid wax core melts and precipitates, and the integrated soft finger is obtained.

The performance and fatigue tests of the soft fingers produced by the proposed method have been carried out. The results show that the manufacturing method can significantly improve the fatigue resistance and stability of the soft fingers, while also avoiding the problems such as air leakage and cracking.

The improvement of the previous multistep casting method of soft fingers is proposed, and the integrated molding manufacturing method is proposed to avoid the problems caused by secondary bonding.

Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).

This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.

Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.

It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

In response to the severe lag in tracking the response of the Stewart stability platform after adding overload, as well as the impact of nonlinear factors such as load and friction on stability accuracy, a new error attenuation function and a parallel stable platform active disturbance rejection control (ADRC) strategy combining cascade extended state observer (ESO) are proposed.

First, through kinematic modeling of the Stewart platform, the relationship between the desired pose and the control quantities of the six hydraulic cylinders is obtained. Then, a linear nonlinear disturbance observer was established to observe noise and load, to enhance the system’s anti-interference ability. Finally, verification was conducted through simulation.

Finally, stability analysis was conducted on the cascaded observer. Experiments were carried out on a parallel stable platform with six degrees of freedom involving rotation and translation. In comparison to traditional PID and ADRC control methods, the proposed control strategy not only endows the stable platform with strong antiload disturbance capability but also exhibits faster response speed and higher stability accuracy.

A new error attenuation function is designed to address the lack of smoothness at d in the error attenuation function of the ADRC controller, reducing the system ripple caused by it. Finally, a combination of linear and nonlinear ESOs is introduced to enhance the system's response speed and its ability to observe noise and load disturbances. Stability analysis of the cascade observer is carried out, and experiments are conducted on a six-degree-of-freedom parallel stable platform with both rotational and translational motion.

The purpose of this research is to determine how supply chain management (SCM) might be less affected by COVID-19 by using innovative technologies such as the Internet of Things (IoT), eco-friendly corporate practices and other digital advancements. It strongly emphasizes the use of technology to improve supply networks’ and Omani firms’ performance.

Using a mixed-methods research strategy, this study integrates both qualitative and quantitative approaches. It involves a survey and interviews with supply chain and IT managers from various industries in Oman to gather data and evaluate the impact of technology on SCM.

This study finds that IoT capabilities, smart technologies (STs) and green practices significantly mitigate COVID-19 impacts on SCM. The performance of the supply chain and the business are both improved by these technologies’ positive effects on integrating various supply chain elements, such as suppliers, internal processes and customer relations.

The main constraint of this study is its concentration on businesses in Oman, potentially restricting the applicability of its findings to broader contexts. Future studies could investigate similar frameworks across various geographic and industry settings.

The findings suggest that incorporating STs into SCM is crucial for enhancing operational efficiency and resilience against disruptions such as COVID-19. This offers valuable insights for managers and policymakers in adopting technology-driven strategies for SCM.

This study highlights the significant role of technology in sustaining supply chains during pandemics, thereby supporting economic stability and societal well-being. It underscores the importance of technological advancements in maintaining supply chain continuity in challenging times.

By empirically examining the effect of emerging technologies on enhancing SCM in the context of the COVID-19 pandemic, specifically in the Oman market, this research makes a unique contribution to the body of knowledge.

The purpose of this paper is to improve the problem of kinematics incompatibility of human–exoskeleton in the existing rigid lower-limb exoskeleton (LLE).

In this paper, following an introduction, the motion characteristics of the human knee joint and the design method of the exoskeleton were introduced. A kinematics model of the LLE based on cross-four-bar linkage was obtained. The structural parameters of the LLE mechanism were optimized by the particle swarm optimization algorithm. The predefined trajectories used in the optimization process were derived from the ankle joint, not the instantaneous center of rotation of the knee joint. Finally, the motion deviation of the optimization result was simulated, and the human–exoskeleton coordination experiment was designed to compare with the traditional single-axis knee joint in terms of comfort and coordination.

The lower limb exoskeleton mechanism obtained in this paper has a good tracking effect on human movement and has been improved in terms of comfort and coordination compared with the traditional single-axis knee joint.

The customized exoskeleton design method introduced in this paper is relatively simple, and the obtained exoskeleton has better movement coordination than the traditional exoskeleton. It can provide a reference for the design of lower limb exoskeleton and lower limb orthosis.

This paper aims to propose and verify an improved cascade active disturbance rejection control (ADRC) scheme based on output redefinition for hypersonic vehicles (HSVs) with nonminimum phase characteristic and model uncertainties.

To handle the nonminimum phase characteristic, a tuning factor stabilizing internal dynamics is introduced to redefine the system output states; its effective range is determined by analyzing Byrnes–Isidori normalized form of the redefined system. The extended state observers (ESOs) are used to estimate the uncertainties, which include matched and mismatched items in the system. The controller compensates observations in real time and appends integral terms to improve robustness against the estimation errors of ESOs.

Theoretical and simulation results show that the stability of internal dynamics is guaranteed by the tuning factor and the tracking errors of external commands are globally asymptotically stable.

The control scheme in this paper is expected to generate a reliable way for dealing with nonminimum phase characteristic and model uncertainties of HSVs.

In the framework of ADRC, a concise form of redefined outputs is proposed, in which the tuning factor performs a decisive role in stabilizing the internal dynamics of HSVs. By introducing an integral term into the cascade ADRC scheme, the compensation accuracy of matched and mismatched disturbances is improved.

Cushioning is a useful material property applicable for a range of applications from medical devices to personal protective equipment. The current ability to apply cushioning in a product context is limited by the appropriateness of available materials, with polyurethane foams being the current gold standard material. The purpose of this study is to investigate additively manufactured flexible printing of scaffold structures as an alternative.

In this study, this study investigates triply periodic minimal surface (TPMS) structures, including Gyroid, Diamond and Schwarz P formed in thermoplastic polyurethane (TPU), as a possible alternative. Each TPMS structure was fabricated using material extrusion additive manufacturing and evaluated to ASTM mechanical testing standard for polymers. This study focuses attention to TPMS structures fabricated for a fixed unit cell size of 10 mm and examine the compressive properties for changes in the scaffold porosity for samples fabricated in TPU with a shore hardness of 63A and 90A.

It was discovered that for increased porosity there was a measured reduction in the load required to deform the scaffold. Additionally, a complex relationship between the shore hardness and the stiffness of a structure. It was highlighted that through the adjustment of porosity, the compressive strength required to deform the scaffolds to a point of densification could be controlled and predicted with high repeatability.

The results indicate the ability to tailor the scaffold design parameters using both 63A and 90A TPU material, to mimic the loading properties of common polyurethane foams. The use of these structures indicates a next generation of tailored cushioning using additive manufacturing techniques by tailoring both geometry and porosity to loading and compressive strengths.