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The purpose of this theoretical paper is to explore how immigrants' home-country entrepreneurial ecosystem (EE) factors impact transnational immigrant entrepreneurs (TIEs). The paper draws on the dual embeddedness and transnational entrepreneurship theories to explore how the home-country EE influences transnational immigrant entrepreneurship (TIE).

This research adopted a qualitative case study methodology involving content analysis of secondary data. It analyzed data set against the existing EE framework to constructively explore the home-country effects.

The findings reveal that all home-country EE domains and associated factors affect TIEs. The paper established six testable propositions with regard to the home-country EE domains: accessible market, human capital, social culture, infrastructure and business support and government policies. A number of new factors were identified for each home-country EE domain. Finally, the paper provided future research directions.

Care has to be taken in generalizing the findings from this research due to the small sample of contemporary Chinese immigrants in Australia and New Zealand. The propositions also require empirical testing.

The findings contribute to the TIE literature by identifying new factors of the home-country EE and presenting testable propositions. The results have impact on immigration policies and programs.

Transnational immigrant entrepreneurship can be a pathway to help immigrants to integrate into mainstream society. The findings from this article indirectly contribute to immigrant social development.

This original article fills research gaps by analyzing how home-country EE elements affect TIE. It reveals that the EE framework is effective for investigating it.

Supplementing an earlier review paper on the internationalization of Chinese small- and medium-sized enterprises (SMEs) (ICS) that covers the period 1991–2012, the purpose of this paper is to examine how research on this topic has thematically expanded in recent years. Specifically, the authors aim to examine the literature between 2013 and 2020, highlight advancements and synthesize potential avenues for future research.

Based on the systematic literature review method (Denyer and Tranfield, 2009; Kraus et al., 2020), the authors considered more than 5,700 peer-reviewed journal articles, of which 107 were included in the narrative synthesis.

Research on the ICS has become more widespread and mature in the period since 2013. First, there are more papers investigating firm-level characteristics and sources of internal capabilities. Second, the state of knowledge regarding social networks and institutional contexts in the internationalization process has increased. Consequently, new knowledge exists regarding push and pull patterns and the role China’s institutional environment is playing. Third, growing interest can be noted in studying entrepreneurship in the context of Chinese SMEs’ global expansion. Additionally, the paper exposes promising areas for future research and suggests more than 20 potential research questions.

This review in the growing debate on the ICS is the first of its kind that consciously drives the work of a previous review study forward. This enables tracking the progress of research (“mapping of the field”) and identifying important avenues for future research that can further advance the debate. The comprehensive review also discovered one relatively new variable – the role of Chinese returnee entrepreneurs – which shows the significant influence on SME internationalization and attracts growing scholarly attention.

This paper aims to propose a learning exponential jerk trajectory planning to suppress the residual vibrations of industrial robots.

Based on finite impulse response filter technology, a step signal with a proper amplitude first passes through two linear filters and then performs exponential filter shaping to obtain an exponential jerk trajectory and cancel oscillation modal. An iterative learning strategy designed by gradient descent principle is used to adjust the parameters of exponential filter online and achieve the maximum vibration suppression effect.

By building a SCARA robot experiment platform, a series of contrast experiments are conducted. The results show that the proposed method can effectively suppress residual vibration compared to zero vibration shaper and zero vibration and derivative shaper.

The idea of the adopted iterative leaning strategy is simple and reduces the computing power of the controller. A cheap acceleration sensor is available because it just needs to measure vibration energy to feedback. Therefore, the proposed method can be applied to production practice.

The purpose of this paper is to reduce the strain and vibration during robotic machining.

An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state.

The proposed intelligent approach can significantly reduce robotic machining strain and vibration.

The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.

The purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.

The robot constant grinding force control algorithm is based on a grinding model and iterative algorithm. During the impact stage, active disturbance rejection control is used to plan the robotic reference contact force, and the robot speed is adjusted according to the error between the robot’s real contact force and the robot’s reference contact force. In the processing stage, an RBF neural network is used to construct a model with the robot's position offset displacement and controlled output, and the increment of control parameters is estimated according to the RBF neural network model. The error of contact force and expected force converges gradually by iterating the control parameters online continuously.

The experimental results show that the normal force overshoot of the robot based on the grinding model and iterative algorithm is small, and the processing convergence speed is fast. The error between the normal force and the expected force is mostly within ±3 N. The normal force based on the force control algorithm is more stable than the normal force based on position control, and the surface roughness of the processed workpiece has also been improved, the Ra value compared with position control has been reduced by 24.2%.

As the proposed approach obtains a constant effect in the impact stage and processing stage of robot grinding and verified by the experiment, this approach can be used for robot grinding for improved machining accuracy.

The purpose of this paper is to propose a precise time-optimal path tracking approach for robots under kinematic and dynamic constraints to improve the work efficiency of robots and guarantee tracking accuracy.

In the proposed approach, the robot path is expressed by a scalar path coordinate and discretized into N points. The motion between two neighbouring points is assumed to be uniformly accelerated motion, so the time-optimal trajectory that satisfies constraints is obtained by using equations of uniformly accelerated motion instead of numerical integration. To improve dynamic model accuracy, the Coulomb and viscous friction are taken into account (while most publications neglect these effects). Furthermore, an iterative learning algorithm is designed to correct model-plant mismatch by adding an iterative compensation item into the dynamic model at each discrete point before trajectory planning.

An experiment shows that compared with the sequential convex log barrier method, the proposed numerical integration-like (NI-like) approach has less computation time and a smoother planning trajectory. Compared with the experimental results before iteration, the torque deviation, tracking error and trajectory execution time are reduced after 10 iterations.

As the proposed approach not only yields a time-optimal solution but also improves tracking performance, this approach can be used for any repetitive robot tasks that require more rapidity and less tracking error, such as assembly.

To improve production efficiency, industrial robots are expected to replace humans to complete the traditional manual operation on grasping, sorting and assembling workpieces. These implementations are closely related to the accuracy of workpiece location. However, workpiece location methods based on conventional machine vision are sensitive to the factors such as light intensity and surface roughness. To enhance the robustness of the workpiece location method and improve the location accuracy, a workpiece location algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed.

The proposed algorithm integrates a weighted bi-directional feature pyramid network into SSD. A feature fusion architecture is structured by the combination of low-resolution, strong semantic features and high-resolution, weak semantic features. The architecture is built through a top-down pathway, bottom-up pathway, lateral connections and skip connections. To avoid treating all features equally, learnable weights are introduced into each feature layer to characterize its importance. More detailed information from the low-level layers is injected into the high-level layers, which could improve the accuracy of workpiece location.

It is found that the maximum location error at the center point calculated from the proposed algorithm is decreased by more than 22% compared with that of the SSD algorithm. Besides, the average location error evolves a decrease by at least 5%. In the trajectory prediction experiment of the workpiece center point, the results of the proposed algorithm demonstrate that the average location error is below 0.13 mm and the maximum error is no more than 0.23 mm.

In this work, a workpiece location algorithm based on improved SSD is developed to extract the center point of the workpiece. The results demonstrate that the proposed algorithm is beneficial for workpiece location. The proposed algorithm can be readily used in a variety of workpieces or adapted to other similar tasks.

Successful sensorless collision detection by a robot depends on the accuracy with which the external force/torque can be estimated. Compared with collaborative robots, industrial robots often have larger parameter values of their dynamic models and larger errors in parameter identification. In addition, the friction inside a reducer affects the accuracy of external force estimation. The purpose of this paper is to propose a collision detection method for industrial robots. The proposed method does not require additional equipment, such as sensors, and enables highly sensitive collision detection while guaranteeing a zero false alarm rate.

The error on the calculated torque for a robot in stable motion is analyzed, and a typical torque error curve is presented. The variational characteristics of the joint torque error during a collision are analyzed, and collisions are classified into two types: hard and soft. A pair of envelope-like lines with an effect similar to that of the true envelope lines is designed. By using these envelope-like lines, some components of the torque calculation error can be eliminated, and the sensitivity of collision detection can be improved.

The proposed collision detection method based on envelope-like lines can detect hard and soft collisions during the motion of industrial robots. In repeated experiments without collisions, the false alarm rate was 0 per cent, and in repeated experiments with collisions, the rate of successful detection was 100 per cent. Compared with collision detection method based on symmetric thresholds, the proposed method has a smaller detection delay and the same detection sensitivity for different joint rotation directions.

A collision detection method for industrial robots based on envelope-like lines is proposed in this paper. The proposed method does not require additional equipment or complex algorithms, and highly sensitive collision detection can be achieved with zero false alarms. The proposed method is low in cost and highly practical and can be widely used in applications involving industrial robots.

Many metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of this paper is to propose a pose estimation method for grasping metal workpieces by industrial robots.

Dual-hypothesis robust point matching registration network (RPM-Net) is proposed to estimate pose from point cloud. The proposed method uses the Point Cloud Library (PCL) to segment workpiece point cloud from scenes and a trained-well robust point matching registration network to estimate pose through dual-hypothesis point cloud registration.

In the experiment section, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor. A data set that contains three subsets is set up on the experimental platform. After training with the emulation data set, the dual-hypothesis RPM-Net is tested on the experimental data set, and the success rates of the three real data sets are 94.0%, 92.0% and 96.0%, respectively.

The contributions are as follows: first, dual-hypothesis RPM-Net is proposed which can realize the pose estimation of discrete and less-textured metal workpieces from point cloud, and second, a method of making training data sets is proposed using only CAD models with the visualization algorithm of the PCL.

In the pursuit of co-exploration, the strength and brokerage dimensions of dyadic ties create a novelty–action trade-off: tie strength facilitates coordination but constraints novelty, while tie brokerage expands knowledge diversity but aggravates coordination difficulty. This study contributes towards a better understanding of this tension by comparing two dimensions of relational ties and examining their contingent values given different environmental factors and exchange characteristics.

The authors used survey data from 194 matched buyer–supplier dyads in China's high-tech industries and employed hierarchical moderated regression analysis to test the proposed hypotheses.

The authors find that compared with tie strength, tie brokerage has a stronger positive effect on co-exploration. Moreover, guanxi importance amplifies the effect of tie strength while decreasing the value of tie brokerage. As market uncertainty increases, the role of tie brokerage becomes more salient. Additionally, tie strength becomes less effective when buyer centralization is high, whereas tie brokerage exerts a stronger impact on co-exploration when an exchange is highly formalized.

This study contributes to the supply chain literature by adopting a relational perspective to integrate relational ties into the study of buyer–supplier co-exploration and by elaborating on the different implications of tie strength and tie brokerage in resolving the novelty–action trade-off. Furthermore, it provides a more nuanced understanding of when distinct dimensions of relational ties are effective, by clarifying boundary conditions in terms of environmental factors and exchange characteristics.