Wenbin Su

Bio: Wenbin Su is an academic researcher from South China University of Technology. The author has contributed to research in topics: Robot & Mobile robot. The author has an hindex of 3, co-authored 4 publications receiving 129 citations.

Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity

Abstract: The powered exoskeleton promises substantial improvements on daily activities of the people who need robots provide assistance. In order to achieve flexible and stable control of a powered lower limb exoskeleton, in this paper, a hybrid control that combines a brain-computer interface (BCI) based on motor imagery (MI) with surface electromyogram (EMG) signals has been developed. We utilized the common spatial pattern (CSP) method to extract the variance of electroencephalogram (EEG) signals and back propagation (BP) neural network to recognize the imagery tasks. Moreover, we have used the strength of EMG signals obtained from upper forearms of subjects to adjust the gait of exoskeleton robots according to real stairs so that subjects can climb stairs easily and stably. The recognized results of EEG and the strength of EMG are used to drive the powered exoskeleton to help subjects climb the stairs by the designed gait synthesis which satisfies the environmental constraint and kinematic constraint. The developed hybrid control strategy has been verified by three healthy subjects, and all subjects can successfully fulfill steadily climbing the stairs, assisted by the powered exoskeleton. The results of the experiment have demonstrated the developed hybrid brain/muscle signals powered robot can effectively enhance human mobility.

Motor-Imagery-Based Teleoperation of a Dual-Arm Robot Performing Manipulation Tasks

Abstract: This paper proposes a brain–computer interface (BCI)-based teleoperation strategy for a dual-arm robot carrying a common object by multifingered hands. The BCI is based on motor imagery of the human brain, which utilizes common spatial pattern method to analyze the filtered electroencephalograph signals. Human intentions can be recognized and classified into the corresponding reference commands in task space for the robot according to phenomena of event-related synchronization/desynchronization, such that the object manipulation tasks guided by human user’s mind can be achieved. Subsequently, a concise dynamics consisting of the dynamics of the robotic arms and the geometrical constraints between the end-effectors and the object is formulated for the coordinated dual arm. To achieve optimization motion in the task space, a redundancy resolution at velocity level has been implemented through neural-dynamics optimization. Extensive experiments have been made by a number of subjects, and the results were provided to demonstrate the effectiveness of the proposed control strategy.

Brain–Computer Interface-Based Stochastic Navigation and Control of a Semiautonomous Mobile Robot in Indoor Environments

Abstract: In this paper, a brain–computer interface (BCI)-based navigation and control strategy is developed for a mobile robot in indoor environments. It combines the simultaneous localization and mapping to achieve the navigation and positioning for a mobile robot in indoor environments, where the RGB landmarks are regarded as the environmental features learned by the FastSLAM algorithm. The online BCI, based on steady-state visually evoked potentials, exploits multivariate synchronization index classification algorithm to analyze the human electroencephalograph (EEG) signals so that the human intention can be recognized accurately, and then the EEG-based motion commands are produced for the mobile robot. Probability potential field approach based on the probability density function of 2-D normal distribution is connected with the brain signals to generate a collision-free trajectory for the mobile robot. The entire system is semiautonomous, since the robot’s low level behaviors are autonomous and the stochastic navigation is executed by the BCI, and it is verified by the extensive experiments involving five volunteers. All the participants can successfully tele-operate the mobile robot, and the experimental results have verified the effectiveness of the proposed approach.

Brain-computer interface based stochastic navigation and control of a semiautonomous mobile robot in an indoor environment

Abstract: In this paper, we propose a brain-computer interface (BCI) based on the control strategy which combines the simultaneous localization and mapping (SLAM) to obtain the unknown environmental information and builds a global environment map for a mobile robot The online BCI analyzes the electroencephalograph (EEG) signals based on steady-state visually evoked potentials (SSVEP) that the human intentions can be recognized accurately, and the motion commands are produced by the multivariate synchronization index (MSI) algorithm Probability potential fields (PPF) approach based on the probability density function of two dimensional normal distribution is connected with the brain signals to produce the motion commands which generate a trajectory without collision The whole system is semi-autonomous when the RGB landmarks are regarded as the environmental features learned by the FastSLAM algorithm, and the robot's low level behaviors are autonomous since the stochastic navigation is executed by the BCI In addition, a kinematic controller is also adopted to control the low level movements The entire system has been tested and the results have verified the effectiveness of the proposed approach

Prediction of Preventive Maintenance Cycle of Smart Meter Based on Weibull Distribution to Reduce Maintenance Cost

Abstract: Electricity meter enterprises do not have enough understanding of the importance of reliability prediction of smart meters and lack of design and research on the reliability life of smart meters, which leads to difficulties in the maintenance and replacement of electricity meters. Therefore, there is an urgent need for a reliable and effective method to estimate the reliable life and remaining life of smart meters, so as to provide decisionmaking basis for the verification and rotation cycle optimization of batch electric meters, improve user experience and save operation and maintenance costs. According to the operation data of smart meters, firstly, the data are processed by drawing Weibull distribution diagram. The results show that the operation time of smart meters follows Weibull distribution; The two parameter Weibull distribution model is established. The parameters of the two parameter Weibull distribution are determined by the least square method and the fitting effect is verified by the K-S test method; And calculated the preventive maintenance cycle under the minimum maintenance cost. When the regular maintenance cost is 40 yuan and the after-maintenance cost is 300 yuan, the minimum maintenance cost per unit time of smart electricity meter is 0.07539 yuan, and the corresponding operation time is about 915 days.

Adaptive Neural Networks Finite-Time Optimal Control for a Class of Nonlinear Systems

Abstract: This article addresses the finite-time optimal control problem for a class of nonlinear systems whose powers are positive odd rational numbers. First of all, a finite-time controller, which is capable of ensuring the semiglobal practical finite-time stability for the closed-loop systems, is developed using the adaptive neural networks (NNs) control method, adding one power integrator technique and backstepping scheme. Second, the corresponding design parameters are optimized, and the finite-time optimal control property is obtained by means of minimizing the well-defined and designed cost function. Finally, a numerical simulation example is given to further validate the feasibility and effectiveness of the proposed optimal control strategy.

Toward Teaching by Demonstration for Robot-Assisted Minimally Invasive Surgery

Abstract: Learning manipulation skills from open surgery provides more flexible access to the organ targets in the abdomen cavity and this could make the surgical robot working in a highly intelligent and friendly manner. Teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. This work aims to transfer motion skills from multiple human demonstrations in open surgery to robot manipulators in robot-assisted minimally invasive surgery (RA-MIS) by using TbD. However, the kinematic constraint should be respected during the performing of the learned skills by using a robot for minimally invasive surgery. In this article, we propose a novel methodology by integrating the cognitive learning techniques and the developed control techniques, allowing the robot to be highly intelligent to learn senior surgeons’ skills and to perform the learned surgical operations in semiautonomous surgery in the future. Finally, experiments are performed to verify the efficiency of the proposed strategy, and the results demonstrate the ability of the system to transfer human manipulation skills to a robot in RA-MIS and also shows that the remote center of motion (RCM) constraint can be guaranteed simultaneously. Note to Practitioners —This article is inspired by limited access to the manipulation of laparoscopic surgery under a kinematic constraint at the point of incision. Current commercial surgical robots are mostly operated by teleoperation, which is representing less autonomy on surgery. Assisting and enhancing the surgeon’s performance by increasing the autonomy of surgical robots has fundamental importance. The technique of teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. With the improved ability to interact with humans, such as flexibility and compliance, the new generation of serial robots becomes more and more popular in nonclinical research. Thus, advanced control strategies are required by integrating cognitive functions and learning techniques into the processes of surgical operation between robots, surgeon, and minimally invasive surgery (MIS). In this article, we propose a novel methodology to model the manipulation skill from multiple demonstrations and execute the learned operations in robot-assisted minimally invasive surgery (RA-MIS) by using a decoupled controller to respect the remote center of motion (RCM) constraint exploiting the redundancy of the robot. The developed control scheme has the following functionalities: 1) it enables the 3-D manipulation skill modeling after multiple demonstrations of the surgical tasks in open surgery by integrating dynamic time warping (DTW) and Gaussian mixture model (GMM)-based dynamic movement primitive (DMP) and 2) it maintains the RCM constraint in a smaller safe area while performing the learned operation in RA-MIS. The developed control strategy can also be potentially used in other industrial applications with a similar scenario.

Fuzzy-Torque Approximation-Enhanced Sliding Mode Control for Lateral Stability of Mobile Robot

Abstract: Accurate path tracking and stability are the main challenges of lateral motion control in mobile robots, especially under the situation with complex road conditions. The interaction force between robots and the external environment may cause interference, which should be considered to guarantee its path tracking performance in dynamic and uncertain environments. In this article, a flexible lateral control scheme is considered for the developed wheel-legged robot, which consists of a cubature Kalman algorithm to evaluate the centroid slip angle and the yaw rate. Furthermore, a fuzzy compensation and preview angle-enhanced sliding model controller to improve the tracking accuracy and robustness. Finally, some simulations and experimental demonstrations using the four-wheel-legged robot (BIT-NAZA) are carried out to illustrate the effectiveness and robustness, and the proposed method has achieved satisfactory results in high-precision trajectory tracking and stability control of the mobile robot.