Weiwei Shang

Bio: Weiwei Shang is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Parallel manipulator & Control theory. The author has an hindex of 19, co-authored 84 publications receiving 1116 citations. Previous affiliations of Weiwei Shang include China University of Science and Technology.

A Review on Cable-driven Parallel Robots

Abstract: Cable-driven parallel robots (CDPRs) are categorized as a type of parallel manipulators. In CDPRs, flexible cables are used to take the place of rigid links. The particular property of cables provides CDPRs several advantages, including larger workspaces, higher payload-to-weight ratio and lower manufacturing costs rather than rigid-link robots. In this paper, the history of the development of CDPRs is introduced and several successful latest application cases of CDPRs are presented. The theory development of CDPRs is introduced focusing on design, performance analysis and control theory. Research on CDPRs gains wide attention and is highly motivated by the modern engineering demand for large load capacity and workspace. A number of exciting advances in CDPRs are summarized in this paper since it is proposed in the 1980s, which points to a fruitful future both in theory and application. In order to meet the increasing requirements of robot in different areas, future steps foresee more in-depth research and extension applications of CDPRs including intelligent control, composite materials, integrated and reconfigurable design.

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.

Active Joint Synchronization Control for a 2-DOF Redundantly Actuated Parallel Manipulator

Abstract: This brief applies the synchronization to solve control problem of redundantly actuated parallel manipulators. With the synchronization method, a new controller termed active joint-synchronization (AJ-S) controller is developed for a 2-degree-of-freedom (DOF) redundantly actuated parallel manipulator. The dynamic model of the parallel manipulator is formulated in the active joint space, in which the internal force is calculated by the projection method and the friction is depicted with the Coulomb + viscous friction model. By defining the tracking error, synchronization error, coupled error, and the referenced trajectory vector of the active joints, the AJ-S controller based on the dynamic model is designed. And the AJ-S controller is proven to guarantee asymptotic convergence to zero of both tracking and synchronization errors by the Barbalat's lemma. The AJ-S controller is implemented in the trajectory tracking experiments of an actual 2-DOF redundantly actuated parallel manipulator, and the superiority of the AJ-S controller over the well-known tracking controller is studied.

Coordination Motion Control in the Task Space for Parallel Manipulators With Actuation Redundancy

Abstract: This paper presents a task space coordination controller for the parallel manipulators with actuation redundancy to improve the motion relation between multiple kinematic chains. According to the mechanism characteristic of multiple kinematic chains, two different types of synchronization error are developed in the joint space of active joints and in the task space of end-effector, respectively. The coordination controller is designed by using the synchronization error, and it is proved to guarantee asymptotic convergence to zero of both tracking error and synchronization error with the Barbalat's Lemma. The trajectory tracking experiments are carried out on an actual parallel manipulator with actuation redundancy, and the superiority of the coordination controller over the traditional augmented PD (APD) controller is studied.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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.

A High-Precision Motion Control Based on a Periodic Adaptive Disturbance Observer in a PMLSM

Abstract: This paper presents a novel disturbance compensation scheme to attenuate periodic disturbances on repetitive motion using permanent magnet linear synchronous motors (PMLSMs), and this scheme is called the periodical adaptive disturbance observer. The scheme is based on assumptions that all measured states and disturbances are periodic and repetitive when the tasks executed by PMLSM motion systems have periodic and repetitive characteristics. In the proposed control scheme, a lumped disturbance is estimated by the classical linear disturbance observer (DOB) for the initial time period and stored in memory storages. It consists of parametric errors multiplied by states, friction force, and force ripple, and then, it is updated for each time period by the periodic adaptation law. This scheme requires no mathematical models of disturbances and adaptation laws of model parameters such as the mass of the mover and viscous friction coefficient. Also, it is possible to compensate for disturbances above as well as below the bandwidth of the Q-filter (LPF) of DOB. The effectiveness of the proposed control scheme is verified by various experiments that take into account varying frequency components of disturbances along the operating speed of a mover of PMLSM such as force ripple and friction force.

A Review on Cable-driven Parallel Robots

Abstract: Cable-driven parallel robots (CDPRs) are categorized as a type of parallel manipulators. In CDPRs, flexible cables are used to take the place of rigid links. The particular property of cables provides CDPRs several advantages, including larger workspaces, higher payload-to-weight ratio and lower manufacturing costs rather than rigid-link robots. In this paper, the history of the development of CDPRs is introduced and several successful latest application cases of CDPRs are presented. The theory development of CDPRs is introduced focusing on design, performance analysis and control theory. Research on CDPRs gains wide attention and is highly motivated by the modern engineering demand for large load capacity and workspace. A number of exciting advances in CDPRs are summarized in this paper since it is proposed in the 1980s, which points to a fruitful future both in theory and application. In order to meet the increasing requirements of robot in different areas, future steps foresee more in-depth research and extension applications of CDPRs including intelligent control, composite materials, integrated and reconfigurable design.