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The purpose of this paper is to develop a vision-based dual-arm cyclic motion method, focusing on solving the problems of an uncertain grasp position of the object and the dual-arm joint-angle-drift phenomenon.

A novel cascade control structure is proposed which associates an adaptive neural network with kinematics redundancy optimization. A radial basis function (RBF) neural network in conjunction with a conventional proportional–integral (PI) controller is applied to compensate for the uncertainty of the image Jacobian matrix which includes the estimated grasp position. To avoid the joint-angle-drift phenomenon, a dual neural network (DNN) solver in conjunction with a PI controller and dual-arm-coordinated constraints is applied to optimize the closed-chain kinematics redundancy.

The proposed method was implemented on an industrial robotic MOTOMAN with two 7-degrees of freedom robotic arms. Two experiments of carrying a tray repeatedly and turning a steering wheel were carried out, and the results indicate that the closed-trajectories tracking is achieved successfully both in the image plane and the joint spaces with the uncertain grasp position, which validates the accuracy and realizability of the proposed PI-RBF-DNN control strategy.

The adaptive neural network visual servoing method is applied to the dual-arm cyclic motion with the uncertain grasp position of the object. The proposed method enhances the environmental adaptability of a dual-arm robot in a practical manipulation task.

The purpose of this paper is to propose a variable impedance control method of finger exoskeleton for hand rehabilitation using the contact forces between the finger and the exoskeleton, making the output trajectory of finger exoskeleton comply with the natural flexion-extension (NFE) trajectory accurately and adaptively.

This paper presents a variable impedance control method based on fuzzy neural network (FNN). The impedance control system sets the contact forces and joint angles collected by sensors as input. Then it uses the offline-trained FNN system to acquire the impedance parameters in real time, thus realizing tracking the NFE trajectory. K-means clustering method is applied to construct FNN, which can obtain the number of fuzzy rules automatically.

The results of simulations and experiments both show that the finger exoskeleton has an accurate output trajectory and an adaptive performance on three subjects with different physiological parameters. The variable impedance control system can drive the finger exoskeleton to comply with the NFE trajectory accurately and adaptively using the continuously changing contact forces.

The finger is regarded as a part of the control system to get the contact forces between finger and exoskeleton, and the impedance parameters can be updated in real time to make the output trajectory comply with the NFE trajectory accurately and adaptively during the rehabilitation.

The accuracy and reliability of upper limb motion assessment have received great attention in the field of rehabilitation. Grasping test is widely carried out for motion assessment, which requires patients to grasp objects and move them to target place. The traditional assessments test the upper limb motion ability by therapists, which mainly relies on experience and lacks quantitative indicators. This paper aims to propose a deep learning method based on the vision system of our upper limb rehabilitation robot to recognize the motion trajectory of rehabilitation target objects automatically and quantitatively assess the upper limb motion in the grasping test.

To begin with, an SRF network is designed to recognize rehabilitation target objects grasped in assessment tests. Moreover, the upper limb motion trajectory is calculated through the motion of objects’ central positions. After that, a GAE network is designed to analyze the motion trajectory which reflects the motion of upper limb. Finally, based on the upper limb rehabilitation exoskeleton platform, the upper limb motion assessment tests are carried out to show the accuracy of both object recognition of SRF network and motion assessment of GAE network. The results including object recognition, trajectory calculation and deviation assessment are given with details.

The performance of the proposed networks is validated by experiments that are developed on the upper limb rehabilitation robot. It is implemented by recognizing rehabilitation target objects, calculating the motion trajectory and grading the upper limb motion performance. It illustrates that the networks, including both object recognition and trajectory evaluation, can grade the upper limb motion functionn accurately, where the accuracy is above 95.0% in different grasping tests.

A novel assessment method of upper limb motion is proposed and verified. According to the experimental results, the accuracy can be remarkably enhanced, and the stability of the results can be improved, which provide more quantitative indicators for further application of upper limb motion assessment.

The purpose of this paper is to propose a robust iterative LIDAR-based pose tracking method assisted by modified visual odometer to resist initial value disturbance and locate a robot in the environments with certain occlusion.

At first, an iterative LIDAR-based pose tracking method is proposed. The LIDAR information is filtered and occupancy grid map is pre-processed. The sample generation and scoring are iterated so that the result is converged to the stable value. To improve the efficiency of sample processing, the integer-valued map indices of rotational samples are preserved and translated. All generated samples are analyzed to determine the maximum error direction. Then, a modified visual odometer is introduced for error compensation. The oriented fast and rotated brief (ORB) features are uniformly sampled in the image. A local map which contains key frames for reference is maintained. These two measures ensure that the modified visual odometer is able to return robust result which compensates the error of LIDAR-based pose tracking method in the maximum error direction.

Three experiments are conducted to prove the advantages of the proposed method. The proposed method can resist initial value disturbance with high computational efficiency, give back credible real-time result in the environment with abundant features and locate a robot in the environment with certain occlusion.

The proposed method is able to give back real-time pose tracking results with robustness. The iterative sample generation enables the robot to resist initial value disturbance. In each iteration, rotational and translational samples are separately generated to enhance computational efficiency. The maximum error direction of LIDAR-based pose tracking method is determined by principle component analysis and compensated by the result of modified visual odometer to give back correct pose in the environment with certain occlusion.

The purpose of this paper is to propose an enhanced pose tracking method using progressive scan matching, focusing on accuracy, time efficiency and robustness.

The general purpose of localization algorithms is to dynamically track a robot instead of globally locating one. In this paper, progressive scan matching is used to promote the performance of pose tracking. Rotational and translational samples are separately generated to accelerate the calculation and to increase the accuracy. Progressive iteration of sample generation can ensure localization to achieve a specific precision. The direction of localization uncertainty is taken into consideration to increase robustness. Nonlinear optimization is adopted to achieve a more precise result.

The proposed method was implemented on a self-made mobile robot. Two experiments were conducted to test the accuracy and time efficiency of the method. The comparison with the basic Monte Carlo localization shows the advantages of the method. Another two experiments were conducted to test the robustness of the method. The result shows that the method can relocate a robot from an inaccurate place if the offset is moderate.

An enhanced pose tracking method is proposed to promote the performance by separately processing rotational and translational samples, progressively iterating the sample generation, taking the direction of localization uncertainty into consideration and adopting nonlinear optimization. The proposed method enables a robot to accurately and quickly locate itself in the environment with robustness.

The purpose of this paper is to propose an error model for serial robot kinematic calibration based on dual quaternions.

The dual quaternions are the combination of dual-number theory and quaternion algebra, which means that they can represent spatial transformation. The dual quaternions can represent the screw displacement in a compact and efficient way, so that they are used for the kinematic analysis of serial robot. The error model proposed in this paper is derived from the forward kinematic equations via using dual quaternion algebra. The full pose measurements are considered to apply the error model to the serial robot by using Leica Geosystems Absolute Tracker (AT960) and tracker machine control (T-MAC) probe.

Two kinematic-parameter identification algorithms are derived from the proposed error model based on dual quaternions, and they can be used for serial robot calibration. The error model uses Denavit–Hartenberg (DH) notation in the kinematic analysis, so that it gives the intuitive geometrical meaning of the kinematic parameters. The absolute tracker system can measure the position and orientation of the end-effector (EE) simultaneously via using T-MAC.

The error model formulated by dual quaternion algebra contains all the basic geometrical parameters of serial robot during the kinematic calibration process. The vector of dual quaternion error can be used as an indicator to represent the trend of error change of robot’s EE between the nominal value and the actual value. The accuracy of the EE is improved after nearly 20 measurements in the experiment conduct on robot SDA5F. The simulation and experiment verify the effectiveness of the error model and the calibration algorithms.

This paper aims to develop a pose/force coordination method for a redundant dual-arm robot to achieve a symmetric coordination task.

A novel control strategy of dual-arm coordination is proposed that associates pose coordination with force coordination. The spatial in-parallel spring and damping model is built to regulate the relative pose error of two end-effectors in real time, and force coordination factor is introduced to realize the dynamic distribution of loadings to limit the object’s internal force in real time.

The proposed method was verified on a real dual-arm robot platform. The symmetric coordination task is performed and the experiment results show that a good behavior on the regulation of the relative pose errors between two arms to achieve the object’s trajectory tracking, and the distribution of the two end-effectors’ loadings to limit the object’s internal force.

The benefits of the proposed method are to improve the object’s tracking performance and avoid the object damage during the symmetric coordination task.

This paper aims to provide a novel obstacle avoidance method based on multi-information inflation map.

In this paper, the multi-information inflation map is introduced, which considers different information, including a two-dimensional grid map and a variety of sensor information. The static layer of the map is pre-processed at first. Then sensor inputs are added in different semantic layers. The processed information in semantic layers is used to update the static layer. The obstacle avoidance algorithm based on the multi-information inflation map is able to generate different avoidance paths for different kinds of obstacles, and the motion planning based on multi-information inflation map can track the global path and drive the robot.

The proposed method was implemented on a self-made mobile robot. Four experiments are conducted to verify the advantages of the proposed method. The first experiment is to demonstrate the advantages of the multi-information inflation map over the layered cost map. The second and third experiments verify the effectiveness of the obstacle avoidance path generation and motion planning. The fourth experiment comprehensively verifies that the obstacle avoidance algorithm is able to deal with different kinds of obstacles.

The multi-information inflation map proposed in this paper has better performance than the layered cost maps. As the static layer is pre-processed, the computational efficiency is higher. Sensor information is added in semantic layers with different cost attenuation coefficients. All layers are reset before next update. Therefore, the previous state will not affect the current situation. The obstacle avoidance and motion planning algorithm based on the multi-information inflation map can generate different paths for different obstacles and drive a robot safely and control the velocity according to different conditions.

The purpose of this paper is to investigate dynamic characteristics of opposed-conical gas-dynamic bearings considering five degree-of-freedom motion, including translation and tilt.

The steady-state Reynolds equation and perturbed Reynolds equations are solved on the surface of conical bearings, and both stiffness and damping coefficients are calculated. A formula for quickly calculating critical mass is deduced to discriminate the stability of the rotor considering the five degree-of-freedom motion.

Results show that the stability of the rotor is mainly determined by translation rather than tilt. The formula of critical mass is validated by comparing the results with traditional Routh–Hurwitz criterion.

The formula proposed in this paper greatly simplifies the solution of critical mass, which facilitates the rotor stability design. It is applicable for opposed-conical bearings, opposed-hemispherical bearings and spherical bearings. The results provide theoretical guidance for the design of gas-dynamic bearings.

The purpose of this paper is to further explore the co-citation and bibliographic-coupling relationship among the core authors in the field of Chinese information science (IS), to expose research activity and author impact, and to make induction analyses about Chinese IS research patterns and theme evolution.

The research data include 8,567 papers and 70,947 cited articles in the IS field indexed by Chinese Social Sciences Citation Index from 2000 to 2009. Author co-citation analysis, author bibliographic-coupling analysis, social network analysis, and factor analysis were combined to explore co-citation and bibliographic-coupling relationships and to identify research groups and subjects.

Scholars with greatest impact are different from the most active scholars of Chinese IS; there is no uniform impact pattern forming since authors’ impact subjects are scattered and not steady; while authors’ research activities present higher independence and concentration, there is still no steady research pattern due to no deep research existing. Furthermore, Chinese IS studies can be delineated by: foundation or extension. The research subjects of these two parts, as well as their corresponding/contributing authors, are different under different views. The general research status of core authors is concentrated, while their impact is broad.

The combined use of some related methods could enrich the development and methodology research of the discipline, and the results establish a reference point on the development of IS research.