Zongwu Xie

Bio: Zongwu Xie is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Robot & Computer science. The author has an hindex of 11, co-authored 69 publications receiving 494 citations.

A Signal-Based Fault Detection and Tolerance Control Method of Current Sensor for PMSM Drive

Abstract: A new fault detection and tolerance (FDT) control strategy for current sensors of permanent magnet synchronous machine drive in filed-oriented control mode was introduced in this paper, based on the reasonable estimation of current amplitude. The propounded method can be applied to deal with a single or double signal-loss faults (two concurrent or independent faults), and shows the capability of online revision to gain variation and zero offset. Compared with the conventional observer-/model-based fault detection methods for current sensors, the proposed method only requires the information of three-phase currents and the position of motor rotor, instead of an accurate system model with explicit parameters. The improved simplicity and reliability makes this new method especially useful for FDT of current sensors in a real-time control system with limited computational capability. The feasibility and robustness of the proposed approach has been validated by extensive experiments under a wide variety of working conditions.

An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics

Abstract: Edge detection is one of the most fundamental operations in the field of image analysis and computer vision as a critical preprocessing step for high-level tasks. It is difficult to give a generic threshold that works well on all images as the image contents are totally different. This paper presents an adaptive, robust and effective edge detector for real-time applications. According to the 2D entropy, the images can be clarified into three groups, each attached with a reference percentage value based on the edge proportion statistics. Compared with the attached points along the gradient direction, anchor points were extracted with high probability to be edge pixels. Taking the segment direction into account, these points were then jointed into different edge segments, each of which was a clean, contiguous, 1-pixel wide chain of pixels. Experimental results indicate that the proposed edge detector outperforms the traditional edge following methods in terms of detection accuracy. Besides, the detection results can be used as the input information for post-processing applications in real-time.

A robot arm/hand teleoperation system with telepresence and shared control

Abstract: This paper describes a master-slave teleoperation system which is developed to evaluate the effectiveness of teleopresence in telerobotics applications. The operator wears a dataglove augmented with an arm-grounded force feedback device to control the dexterous hand and utilizes a spaceball to control robot arm. Contact forces measured by the finger sensors can be feedback to the operator and visual telepresence systems collect the remote operation scenes and display to the operator by a stereo helmet. A primitive autonomous grasp system based on parallel joint torque/position control is developed. The experimental results show that this teleoperation system is intuitive and productive and the primitive autonomous grasp is feasible and efficient

EMG Control for a Five-fingered Underactuated Prosthetic Hand Based on Wavelet Transform and Sample Entropy

Abstract: A new five-fingered underactuated prosthetic hand control system is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines VLR (variable learning rate) based neural network with wavelet transform and sample entropy. This motion pattern classifier can successfully identify flexion and extension of the thumb, the index finger and the middle finger, by measuring the surface EMG signals through three electrodes mounted on the flexor digitorum profundus, flexor pollicis longus and extensor digitorum. Furthermore, via continuously controlling single finger's motion, the prosthetic hand can achieve more prehensile postures such as power grasp, fingertip grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its high recognition capability.

An exoskeleton master hand for controlling DLR/HIT hand

Abstract: In order to eliminate the drawbacks of conventional force feedback gloves, a new type of master hand has been developed. By utilizing three “four-bar mechanism joint” in series and wire coupling mechanism, the master finger transmission ratio is kept exact 1:1.4:1 in the whole movement range and it can make active motions in both extension and flexion direction. Additionally, to assure faster data transmission and near zero delay in master-slave operation, a digital signal processing/field programmable gate array (DSP/FPGA-FPGA) structure with 200µs cycle time is designed. The operating modes of the master hand can be contact or non-contact, which depends on the motion states of slave hand, free motion or constrained motion. The position control employed in non-contact mode ensures unconstrained motion and the force control adopted in contact mode guarantees natural contact sensation. To evaluate the performances of the master hand, an master-slave control experiment based on Force-Position control method between the master hand and DLR/HIT hand is conducted. The results demonstrate this new type master hand can augment telepresence.

Myoelectric control systems—A survey

Abstract: The development of an advanced human–machine interface has always been an interesting research topic in the field of rehabilitation, in which biomedical signals, such as myoelectric signals, have a key role to play. Myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in pattern recognition- and non-pattern recognition-based myoelectric control, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

Cybernetics, or Control and Communication in the Animal and the Machine

Abstract: (1963). Cybernetics, or Control and Communication in the Animal and the Machine. Technometrics: Vol. 5, No. 1, pp. 128-130.

EMG feature evaluation for improving myoelectric pattern recognition robustness

Abstract: In pattern recognition-based myoelectric control, high accuracy for multiple discriminated motions is presented in most of related literature. However, there is a gap between the classification accuracy and the usability of practical applications of myoelectric control, especially the effect of long-term usage. This paper proposes and investigates the behavior of fifty time-domain and frequency-domain features to classify ten upper limb motions using electromyographic data recorded during 21days. The most stable single feature and multiple feature sets are presented with the optimum configuration of myoelectric control, i.e. data segmentation and classifier. The result shows that sample entropy (SampEn) outperforms other features when compared using linear discriminant analysis (LDA), a robust classifier. The averaged test classification accuracy is 93.37%, when trained in only initial first day. It brings only 2.45% decrease compared with retraining schemes. Increasing number of features to four, which consists of SampEn, the fourth order cepstrum coefficients, root mean square and waveform length, increase the classification accuracy to 98.87%. The proposed techniques achieve to maintain the high accuracy without the retraining scheme. Additionally, this continuous classification allows the real-time operation.

Biomechatronic Design and Control of an Anthropomorphic Artificial Hand for Prosthetic and Robotic Applications

Abstract: This paper proposes a biomechatronic approach to the design of an anthropomorphic artificial hand able to mimic the natural motion of the human fingers. The hand is conceived to be applied to prosthetics as well as to humanoid and personal robotics; hence, anthropomorphism is a fundamental requirement to be addressed both in the physical aspect and in the functional behavior. In this paper, a biomechatronic approach is addressed to harmonize the mechanical design of the anthropomorphic artificial hand with the design of the hand control system. More in detail, this paper focuses on the control system of the hand and on the optimization of the hand design in order to obtain a human-like kinematics and dynamics. By evaluating the simulated hand performance, the mechanical design is iteratively refined. The mechanical structure and the ratio between number of actuators and number of degrees of freedom (DOFs) have been optimized in order to cope with the strict size and weight constraints that are typical of application of artificial hands to prosthetics and humanoid robotics. The proposed hand has a kinematic structure similar to the natural hand featuring three articulated fingers (thumb, index, and middle finger with 3 DOF for each finger and 1 DOF for the abduction/adduction of the thumb) driven by four dc motors. A special underactuated transmission has been designed that allows keeping the number of motors as low as possible while achieving a self-adaptive grasp, as a result of the passive compliance of the distal DOF of the fingers. A proper hand control scheme has been designed and implemented for the study and optimization of hand motor performance in order to achieve a human-like motor behavior. To this aim, available data on motion of the human fingers are collected from the neuroscience literature in order to derive a reference input for the control. Simulation trials and computer-aided design (CAD) mechanical tools are used to obtain a finger model including its dynamics. Also the closed-loop control system is simulated in order to study the effect of iterative mechanical redesign and to define the final set of mechanical parameters for the hand optimization. Results of the experimental tests carried out for validating the model of the robotic finger, and details on the process of integrated refinement and optimization of the mechanical structure and of the hand motor control scheme are extensively reported in the paper.

Current state of digital signal processing in myoelectric interfaces and related applications

Abstract: This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.