Hong Zeng

Safety Supervisory Strategy for an Upper-Limb Rehabilitation Robot Based on Impedance Control

User security is an important consideration for robots that interact with humans, especially for upper-limb rehabilitation robots, during the use of which stroke patients are often more susceptible to injury. In this paper, a novel safety supervisory control method incorporating fuzzy logic is proposed so as to guarantee the impaired limbs safety should an emergency situation occur and the robustness of the upper-limb rehabilitation robot control system. Firstly, a safety supervisory fuzzy controller (SSFC) was designed based on the impaired-limbs real-time physical state by extracting and recognizing the impaired-limbs tracking movement features. Then, the proposed SSFC was used to automatically regulate the desired force either to account for reasonable disturbance resulting from pose or position changes or to respond in adequate time to an emergency based on an evaluation of the impaired-limbs physical condition. Finally, a position-based impedance controller was implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The experimental results show the effectiveness and potential of the proposed method for achieving safety and robustness for the rehabilitation robot.

In Situ Regolith Bulk Density Measurement for a Coiling-Type Sampler

AbstractMeasuring regolith density in outer space has always been a difficult problem. In this paper, a bulk density measuring method for planetary regolith based on vibration is proposed. The bulk density measurement of planetary regolith being sampled is realized mainly by vibration signal processing and general regression neural network identification. The method makes full use of the special structure of a mini coiling-type sampler, which utilizes the flexible coiling spring as its sampling arm. To begin, vibration signals are acquired and their trends are removed. Then, multiresolution wavelet analysis is used to filter out the signals that are not related to bulk density. After that, two of the power spectrum estimation methods, Periodogram and Welch, are introduced to extract the density-related features of the vibration signals and reduce the dimensions of them. The multiclass classification support vector machine is then adopted to verify the relationship between the bulk densities of regoliths a...

3D-point-cloud registration and real-world dynamic modelling-based virtual environment building method for teleoperation- CORRIGENDUM

It is a challenging task for a human operator to manipulate a robot from a remote distance, especially in an unknown environment. Excellent teleoperation provides the human operator with a sense of telepresence, mainly including real-world vision, haptic perception, etc. This paper presents a novel virtual environment building method using the red–green–blue (RGB) colour information, the surface normal feature-based 3D-point-cloud registration method and the weighted sliding-average least-square-method-based real-world dynamic modelling for teleoperation. The experiments prove the method to be an accurate and effective means of teleoperation.

Robotic arm control using hybrid brain-machine interface and augmented reality feedback

Brain-machine interface (BMI) can be used to control robotic arm to assist paralysis people improving their quality of life. However process control of objects grasping is still a complex task for BMI users. High efficiency and accuracy is hard to achieve in objects grasping process even after extensive training. An important reason is lack of sufficient feedback information for performing the closed-loop control. In this study, we describe a method of augmented reality (AR) guiding assistance to provide extra feedback information to the user for closed-loop control. A hybrid BMI based system with AR feedback is proposed to evaluate the performance of our method in objects grasping task using robotic arm. Reaching and releasing tasks are completed by the robotic arm automatically. For the grasping task controlled by the user, AR is used to enrich the normal visual information during the grasping process to provide the BMI user augmented feedback information about the gripper status in real time. The feasibility of the proposed system both in open-loop (visual inspection) and closed-loop (AR feedback) are compared. According to our experimental results obtained from 5 subjects, the time used for controlling the robotic arm to grasp objects with AR feedback reduces more than 5s and the error rate of the gripper aperture decreases approximately 20% compared to those of grasping with normal visual inspection only. The results reveal that the BMI user can benefit from the information provided by AR interface in the grasping task.

Robotic neurorehabilitation system design for stroke patients

In this article, a neurorehabilitation system combining robot-aided rehabilitation with motor imagery–based brain–computer interface is presented. Feature extraction and classification algorithm for the motor imagery electroencephalography is implemented under our brain–computer interface research platform. The main hardware platform for functional recovery therapy is the Barrett Whole-Arm Manipulator. The mental imagination of upper limb movements is translated to trigger the Barrett Whole-Arm Manipulator Arm to stretch the affected upper limb to move along the predefined trajectory. A fuzzy proportional–derivative position controller is proposed to control the Whole-Arm Manipulator Arm to perform passive rehabilitation training effectively. A preliminary experiment aimed at testing the proposed system and gaining insight into the potential of motor imagery electroencephalography-triggered robotic therapy is reported.

Virtual exoskeleton-driven uncalibrated visual servoing control for mobile robotic manipulators based on human–robot–robot cooperation

This paper presents an uncalibrated visual servoing control system based on the human–robot–robot cooperation (HRRC). In case of malfunctions of the joint sensors of a robotic manipulator, the proposed system enables the mobile robot to continue operating the manipulator to complete the task that requires careful handling. With the aid of a virtual exoskeleton, an operator may use a human–computer interaction (HCI) device to guide the malfunctioning manipulator. During the guiding process, the virtual exoskeleton serves as a connector between the HCI device and the manipulator. However, when using the HCI device to guide the virtual exoskeleton, there could be a risk of a large-residual problem at any time caused by non-uniform guiding. To solve this problem, a residual switching algorithm (RSA) has been proposed that can identify whether the residual should be calculated based on the motion characteristics of the artificial guiding, reducing the computational cost and ensuring the tracking stability. To enhance the virtual exoskeleton’s ability to drive the manipulator, a multi-joint fuzzy driving controller has been proposed, which can drive the corresponding joint of the manipulator in accordance with an offset vector between the virtual exoskeleton and the manipulator. Lastly, the guiding experiments have verified that, compared with the contrast algorithm, the proposed RSA has a better tracking performance. A peg-in-hole assembly experiment has shown that the proposed control system can assist the operator to control efficiently the robotic manipulator with malfunctioning joint sensors.

Investigation of the phase feature of low-frequency electroencephalography signals for decoding hand movement parameters

The utility to decode hand movement parameters is significant to the control of artificial limb in the BCI fields. Most previous studies have adopted amplitude features of the low-frequency EEG signals to decode hand movement parameters. In this study, we have investigated the instantaneous phase of the low-frequency EEG signals attained by Hilbert transform for such a task for the first time, and compared its decoding accuracy with that of the amplitude features. An experiment was carried out that 5 subjects executed a center-out reaching task in two sessions. Then the Multiple Linear Regression (MLR) model is used to decode hand movement parameters based on the amplitude feature and the phase feature, respectively. The performance of the proposed approach is evaluated by calculating the correlation coefficients between the recorded parameters and the reconstructed parameters. The experiments results show that compared to the decoder with the amplitude feature, the correlation coefficients obtained by the decoder with the phase feature have increased 27.8% (X-position), 24.1% (Y-position), 27.9% (X-velocity), 20.9% (Y-velocity).

Investigation of Phase Features of Movement Related Cortical Potentials for Upper-Limb Movement Intention Detection

The movement related cortical potential (MRCP) is a well-known neural signature of humans self-paced movement intention, which can be exploited by future rehabilitation robots. Most existing studies have explored the amplitude representation for the detection. In this paper we have investigated the phase representation for such a task. On the data sets in which 15 healthy subjects executed a self-initiated upper limb center-out reaching task, we have evaluated the detection models with MRCP amplitude features, MRCP phase features and a concatenation of MRCP amplitude and phase features, respectively. The experimental results have demonstrated that the detector based on the concatenation of amplitude and phase features has not only attained the largest percentage of correct classified trials among the three models (88.05% ± 8.80% of trials), but also achieved the earliest detection of the upper-limb movement intention before the actual movement onset (634.58 ± 211.12 ms before the movement onset).

Construction and Experimental Study of a 3-Dof Haptic Master for Interactive Operation

This paper presents a novel 3-degrees-of-freedom (3-DOF) haptic master with 9 rubber bands for self-resetting. The mechanical design avoids coupling between three 10 directions mechanically by using three perpendicular axis intersecting at one point. Bevel 11 gear transmission is adopted to increase the compactness of the overall structure. 12 VR-based interactive system is designed and built by incorporating the proposed haptic 13 master. The proposed haptic device can generate force feedback along 3-degree-of-freedom 14 motion using motors and provide command signals to the avatar in the virtual 15 environment. In order to analyze the performance of the developed device in terms of 16 haptic feedback operation, ergonomics assessments are designed and experimentally 17 implemented. Preliminary studies on the influencing factor including the guidance force, 18 the reset force, the speed of the avatar and the arm the length have been conducted. The 19 results of this paper are of great significance for the design of the haptic master and 20 interactive system. 21

Grip Force and 3D Push-Pull Force Estimation Based on sEMG and GRNN

The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space) from the electromyogram (EMG) signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG) and the Generalized Regression Neural Network (GRNN) is proposed to meet the requirements of the force control of the intelligent EMG prosthetic hand. Firstly, the experimental platform, the acquisition of the sEMG, the feature extraction of the sEMG and the construction of GRNN are described. Then, the multi-channels of the sEMG when the hand is moving are captured by the EMG sensors attached on eight different positions of the arm skin surface. Meanwhile, a grip force sensor and a three dimension force sensor are adopted to measure the output force of the humans hand. The characteristic matrix of the sEMG and the force signals are used to construct the GRNN. The mean absolute value and the root mean square of the estimation errors, the correlation coefficients between the actual force and the estimated force are employed to assess the accuracy of the estimation. Analysis of variance (ANOVA) is also employed to test the difference of the force estimation. The experiments are implemented to verify the effectiveness of the proposed estimation method and the results show that the output force of the humans hand can be correctly estimated by using sEMG and GRNN method.