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This study aims to present a type of metamorphic mechanism-based quadruped crawling robot. The trunk design of the robot has a metamorphic mechanism, which endows it with excellent crawling capability and adaptability in challenging environments.

The robot consists of a metamorphic trunk and four series-connected three-joint legs. First, the walking and steering strategy is planned through the stability and mechanics analysis. Then, the walking and steering performance is examined using virtual prototype technology, as well as the efficacy of the walking and turning strategy.

The metamorphic quadruped crawling robot has wider application due to its variable trunk configuration and excellent leg motion space. The robot can move in two modes (constant trunk and trunk configuration transformation, respectively, while walking and rotating), which exhibits outstanding stability and adaptability in the examination and verification of prototypes.

The design can enhance the capacity of the quadruped crawling robot to move across a complex environment. The virtual prototype technology verifies that the proposed walking and steering strategy has good maneuverability and stability, which considerably expands the application opportunity in the fields of complicated scene identification and investigation.

The computing power of the legged robot is not enough to perform high-frequency updates for the full-body model predictive control (MPC) of the robot, which is a common problem encountered in the gait research of the legged robot. The purpose of this paper is to propose a high-frequency MPC control method for the bounding gait of a parallel quadruped robot.

According to the bounding gait characteristics of the robot, the quadruped robot model is simplified to an equivalent plane bipedal model. Under the biped robot model, the forces between the robot’s feet and the ground are calculated by MPC. Then, the authors apply a proportional differential controller to distribute these forces to the four feet of the quadruped robot. The robot video can be seen at www.bilibili.com/video/BV1je4y1S7Rn.

To verify the feasibility of the controller, a prototype was made, and the controller was deployed on the actual prototype and then fully analyzed through experiments. Experiments show that the update frequency of MPC could be stabilized at 500 Hz while the robot was running in the bounding gait stably and efficiently.

This paper proposes a high-frequency MPC controller under the simplified model, which has a higher working efficiency and more stable control performance.

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

This paper aims to focus on the spatial docking task of unmanned vehicles under ground conditions. The docking task of military unmanned vehicle application scenarios has strict requirements. Therefore, how to design a docking robot mechanism to achieve accurate docking between vehicles has become a challenge.

In this paper, first, the docking mechanism system is described, and the inverse kinematics model of the docking robot based on Stewart is established. Second, the genetic algorithm-based optimization method for multiobjective parameters of parallel mechanisms including workspace volume and mechanism flexibility is proposed to solve the problem of multiparameter optimization of parallel mechanism and realize the docking of unmanned vehicle space flexibility. The optimization results verify that the structural parameters meet the design requirements. Besides, the static and dynamic finite element analysis are carried out to verify the structural strength and dynamic performance of the docking robot according to the stiffness, strength, dead load and dynamic performance of the docking robot. Finally, taking the docking robot as the experimental platform, experiments are carried out under different working conditions, and the experimental results verify that the docking robot can achieve accurate docking tasks.

Experiments on the docking robot that the proposed design and optimization method has a good effect on structural strength and control accuracy. The experimental results verify that the docking robot mechanism can achieve accurate docking tasks, which is expected to provide technical guidance and reference for unmanned vehicles docking technology.

This research can provide technical guidance and reference for spatial docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

As a new research interest, robots have surpassed human performance across several aspects. In this research, the authors wish to investigate whether robot adopters perform better than non-adopters in terms of export behavior, especially when distinguishing between different types of firms.

The authors try a new strategy to identify the extent of robot adoption by import data and compare the export trajectories of robot adopters and non-adopters by employing the propensity score matching-difference in difference (PSM-DID) method.

The authors find that robot adopters are more likely to enter export markets and improve subsequent export performance, as the gains from doing so can spread the reduction in variable production costs to a larger customer base abroad. But this rule does not always seem to work; for large-scale firms, robot adoption makes it easier to win export competition and increase market share, while small and medium-sized enterprises (SMEs) do not seem to enjoy any benefits from adoption. More importantly, robot adoption also leads to the fiercer market competition when improving the productivity of firms, which will threaten smaller non-adopters.

The findings provide new evidence for the scale bias of robotics and offer new insights into whether exporters or future exporters ought to adopt robots in production.

1. First, distinguishing from existing research, we explain the controversial results of previous work on robotics by providing evidence from export markets and using the concept of size bias, which helps to update the theoretical interpretation of robotics and provides new insights for current and future exporters to evaluate their robot adoption decisions.

2. Second, we extend previous research by further considering the potential robotics threats faced by non-adopters, especially we record that export gains of robot adopters are partially at the expense of smaller non-adopters, which provides new evidence for the rationale of SME protection policies and supplements robotics theory with new knowledge, such as the competitive game of firms related to robot adoption.

3. Third, to our knowledge, prior research tended to examine the economic effects of robotics through industry data provided by the IFR, this may lead to systematic bias due to the inability to distinguish the robot adoption intentions of different firms. In this respect, we try a new strategy through robot import data and further distinguish between robot adopters and non-adopters in the sample, which helps to mitigate the potential bias in the findings and provide a complement to the recently developed literature related to robotics.

4. Finally, as we pointed out earlier, robot adoption could be an interesting research work for the Chinese export market, which helps us to obtain some special findings, such as in assessing whether the benefits of robots are equally appropriate for economies that previously had an advantage in terms of labor.

First, distinguishing from existing research, we explain the controversial results of previous work on robotics by providing evidence from export markets and using the concept of size bias, which helps to update the theoretical interpretation of robotics and provides new insights for current and future exporters to evaluate their robot adoption decisions.

Second, we extend previous research by further considering the potential robotics threats faced by non-adopters, especially we record that export gains of robot adopters are partially at the expense of smaller non-adopters, which provides new evidence for the rationale of SME protection policies and supplements robotics theory with new knowledge, such as the competitive game of firms related to robot adoption.

Third, to our knowledge, prior research tended to examine the economic effects of robotics through industry data provided by the IFR, this may lead to systematic bias due to the inability to distinguish the robot adoption intentions of different firms. In this respect, we try a new strategy through robot import data and further distinguish between robot adopters and non-adopters in the sample, which helps to mitigate the potential bias in the findings and provide a complement to the recently developed literature related to robotics.

Finally, as we pointed out earlier, robot adoption could be an interesting research work for the Chinese export market, which helps us to obtain some special findings, such as in assessing whether the benefits of robots are equally appropriate for economies that previously had an advantage in terms of labor.

The purpose of this paper is to propose a method for selecting the position and attitude trajectory of error measurement to improve the kinematic calibration efficiency of a one translational and two rotational (1T2R) parallel power head and to improve the error compensation effect by improving the properties of the error identification matrix.

First, a general mapping model between the endpoint synthesis error is established and each geometric error source. Second, a model for optimizing the position and attitude trajectory of error measurement based on sensitivity analysis results is proposed, providing a basis for optimizing the error measurement trajectory of the mechanism in the working space. Finally, distance error measurement information and principal component analysis (PCA) ideas are used to construct an error identification matrix. The robustness and compensation effect of the identification algorithm were verified by simulation and through experiments.

Through sensitivity analysis, it is found that the distribution of the sensitivity coefficient of each error source in the plane of the workspace can approximately represent its distribution in the workspace, and when the end of the mechanism moves in a circle with a large nutation angle, the comprehensive influence coefficient of each sensitivity is the largest. Residual analysis shows that the robustness of the identification algorithm with the idea of PCA is improved. Through experiments, it is found that the compensation effect is improved.

A model for optimizing the position and attitude trajectory of error measurement is proposed, which can effectively improve the error measurement efficiency of the 1T2R parallel mechanism. In addition, the PCA idea is introduced. A least-squares PCA error identification algorithm that improves the robustness of the identification algorithm by improving the property of the identification matrix is proposed, and the compensation effect is improved. This method has been verified by experiments on 1T2R parallel mechanism and can be extended to other similar parallel mechanisms.

Compared with the high stiffness of traditional CNC machine tools, the structural stiffness of industrial robots is usually less than 1 N/µm. Chatter not only affects the quality of robotic milling but also reduces the accuracy of the milling process. The purpose of this paper is to reduce chatter in the robotic machining process.

First, the mode coupling chatter mechanism is analyzed. Then the milling force model and the principal stiffness model are established. Finally, the robot milling stability optimization method is proposed. The method considered functional redundancies, and a new robot milling stability index is proposed to improve the quality of milling operations.

The experimental results prove a significant reduction in force fluctuations and surface roughness after using the proposed robotic milling stability optimization method.

In this paper, a new robot milling stability index and a new robot milling stability optimization method are proposed. This method can significantly increase the milling stability and improve the milling quality, which can be widely used in the industry.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

In large equipment and highly complex confined workspaces, the maintenance is usually carried out by snake-arm robots with equal cross-sections. However, the equal cross-sectional design results in the snake arm suffering from stress concentration and restricted working space. The purpose of this paper is to design a variable cross-section elephant trunk robot (ETR) that can address these shortcomings through bionic principles.

This paper proposes a cable-driven ETR to explore the advantages and inspiration of variable cross-section features for hyper-redundant robot design. For the kinematic characteristics, the influence of the variable cross-section design on the maximum joint angle of the ETR is analysed using the control variables method and the structural parameters are selected. Based on the biological inspiration of the whole elephant trunk following the movement of the trunk tip, a trajectory-tracking algorithm is designed to solve the inverse kinematics of the ETR.

Simulation and test results show the unique advantages of the proposed variable cross-section ETR in kinematics and forces, which can reduce stress concentrations and increase the flexibility of movement.

This paper presents a design method for a variable cross-section ETR for confined working spaces, analyses the kinematic characteristics and develops a targeted trajectory control algorithm.

In complex environments, a spherical robot has great application value. When the pendulum spherical robot is stopped or disturbed, there will be a periodic oscillation. This situation will seriously affect the stability of the spherical robot. Therefore, this paper aims to propose a control method based on backstepping and disturbance observers for oscillation suppression.

This paper analyzes the mechanism of oscillation. The oscillation model of the spherical robot is constructed and the relationship between the oscillation and the internal structure of the sphere is analyzed. Based on the oscillation model, the authors design the oscillation suppression control of the spherical robot using the backstepping method. At the same time, a disturbance observer is added to suppress the disturbance.

It is found that the control system based on backstepping and disturbance observer is simple and efficient for nonlinear models. Compared with the PID controller commonly used in engineering, this control method has a better control effect.

The proposed method can provide a reliable and effective stability scheme for spherical robots. The problem of instability in real motion is solved.

In this paper, the oscillation model of a spherical robot is innovatively constructed. Second, a new backstepping control method combined with a disturbance observer for the spherical robot is proposed to suppress the oscillation.