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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.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

The purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.

Human behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.

The author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.

This paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

This paper takes the manufacturing cluster supply chain as the research object and explores the evaluation and enhancement strategy of manufacturing cluster supply chain synergy. The purpose of this study was to (1) analyze the mechanism of manufacturing cluster supply chain synergy; (2) construct manufacturing cluster supply chain synergy evaluation model; (3) algorithm realization of manufacturing cluster supply chain synergy evaluation and (4) propose manufacturing cluster-based supply chain synergy enhancement strategy.

Breaking through the limitations of traditional manufacturing cluster supply chain synergy evaluation, we take horizontal synergy and vertical synergy as coupled synergy subsystems, use the complex system synergy model to explore the horizontal synergy between core enterprises and cluster enterprises and the vertical synergy of supply chain enterprises and use the coupling coordination model to construct the coupled synergy evaluation model of manufacturing cluster supply chain, which is an innovation of the evaluation perspective of previous cluster supply chain synergy and also an enrichment and supplementation of the evaluation methodology. This is not only the innovation of the evaluation perspective but also the enrichment and supplementation of the evaluation method.

Using Python software to conduct empirical analysis on the evaluation model, the research shows that the horizontal and vertical synergies of the manufacturing cluster supply chain interact with each other and jointly affect the coupling synergy. On this basis, targeted strategies are proposed to enhance the synergy of the manufacturing cluster supply chain.

This study takes manufacturers, suppliers and sellers in the three-level supply chain as the research object and does not consider the synergistic evaluation between distributors and consumers in the supply chain, which can be further explored in this direction in the future.

Advanced manufacturing clusters, as the main force of manufacturing development, and the synergistic development of supply chain are one of the important driving forces for the high-quality development of China’s manufacturing industry. As a new type of network organization coupling industrial clusters and supply chains, cluster supply chain is conducive not only to improving the competitiveness of cluster supply chains but also to upgrading cluster supply chains through horizontal synergy within the cluster and vertical synergy in the supply chain.

Research can help accelerate the transformation and upgrading of clustered supply chains in the manufacturing industry, promote high-quality development of the manufacturing industry and accelerate the rise of the global value chain position of the manufacturing industry.

(1) Innovation of research perspective. Starting from two perspectives of horizontal synergy and vertical synergy, we take a core enterprise in the cluster supply chain as the starting point, horizontally explore the main enterprises of the cluster as the research object of horizontal synergy, vertically explore the upstream and downstream enterprises of the supply chain as the research object of vertical synergy and explore the coupling synergy of cluster supply chain as two subsystems, which provides new perspectives of evaluation of the degree of synergy and synergy evaluation. (2) Innovation of research content. Nine manufacturing clusters are selected as research samples, and through data collection and model analysis, it is verified that the evaluation model and implementation algorithm designed in this paper have strong practicability, which not only provides methodological reference for the evaluation of manufacturing cluster-type supply chain synergy but also reduces the loss caused by the instability of clusters and supply chains and then provides a theoretical basis for improving the overall performance of cluster-type supply chains.

Social media played an irreplaceable role in young people’s online social life and information consumption during the COVID-19 pandemic. This research focuses on the impact of excessive information on social media about COVID-19 vaccines on Generation Z's (Gen Z) associated psychological states and long-term vaccine advocacy.

The research conducted structural equation modeling analysis with online survey data from 409 Gen Z citizens in the UK.

The findings suggest that excessive information increased Gen Z social media users' ambivalence and conspiracy beliefs around COVID-19 vaccines, which, in turn, reduced their long-term vaccine advocacy in terms of vaccine acceptance, vaccination intention and vaccine promotion. Importantly, Gen Z’s confidence in government and in the healthcare systems during COVID-19 was effective in helping them overcome the detrimental effects of conspiracy beliefs and ambivalence about long-term vaccine advocacy, respectively.

This research reveals the “dark side” of social media use in the post-pandemic period and highlights the significant roles played by social institutions in mitigating the detrimental effects of Gen Z’s support in social decisions. Beyond the context of COVID-19, this research has important implications for facilitating the civic engagement of Gen Z and boosting their confidence in social institutions in terms of social cohesion.

The purpose of this study is to reduce the residual stress in welded workpieces, optimize the vibratory stress relief treatment process through the use of a vibration generator and enhance the durability and longevity of the workpiece by developing a vibratory stress relief robot that incorporates a multi-manipulator system.

The multi-manipulator combination work is designed so that each manipulator is deployed according to the requirements of vibration stress relief work. Each manipulator works independently and coordinates with others to achieve multi-dimensional vibratory stress relief of the workpiece. A two-degree-of-freedom mobile platform is designed to enable the transverse and longitudinal movement of the manipulator, expanding the working space of the robot. A small electromagnetic superharmonic vibration generator is designed to produce directional vibrations in any orientation. This design addresses the technical challenge of traditional vibration generators being bulky and unable to achieve directional vibrations.

The residual stress relief experiment demonstrates that the residual stress of the workpiece is reduced by approximately 73% through three-degree-of-freedom vibration. The multi-dimensional vibration effectively enhances the relief effect of residual stress, which is beneficial for improving the strength and service life of the workpiece.

A new multi-manipulator robot is proposed to alleviate the residual stress generated by workpiece welding by integrating vibratory stress relief with robotics. It is beneficial to reduce material and energy consumption while enhancing the strength and service life of the workpiece.

The purpose of this study is to present a novel extended hybrid analytical method (HAM) that leverages a two-dimensional (2-D) coupling between the semi-analytical Maxwell–Fourier analysis and the finite element method (FEM) in Cartesian coordinates.

The proposed model is applied to flat permanent-magnet linear electrical machines with rotor-dual. The magnetic field solution across the entire machine is established by coupling an exact analytical model (AM), designed for regions with relative magnetic permeability equal to unity, with a FEM in ferromagnetic regions. The coupling between AM and FEM occurs bidirectionally (x, y) along the edges separating teeth regions and their adjacent regions through applied boundary conditions.

The developed HAM yields accurate results concerning the magnetic flux density distribution, cogging force and induced voltage under various operating conditions, including magnetic or geometric parameters. A comparison with hybrid finite-difference and hybrid reluctance network methods demonstrates very satisfactory agreement with 2-D FEM.

The original contribution of this paper lies in establishing a direct coupling between the semi-analytical Maxwell–Fourier analysis and the FEM, particularly at the interface between adjacent regions with differing magnetic parameters.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.