Abstract:For robot interaction control, the interaction force between the robot and the manipulated object or environment should be monitored. Impedance control is a type of interaction control. Specifically, in impedance control, the dynamic relationship between the interaction force and the resulting motion is controlled. In order to control the impedance of a mechanical system, typically, the interaction force has to be sensed. Due to the inherent limitations of direct force sensing at the interaction site, in the present work, the interaction force is observed using robust observers. In particular, to enhance the accuracy of impedance control, a first order sliding mode impedance controller is designed and incorporated in the present paper. Its advantage over position-based interaction control algorithms is demonstrated through experimentation. Experimental results are given to show the effectiveness of the proposed algorithms.

Abstract:This paper presents a fault diagnosis and fault-tolerant control algorithm, which can be used for a class of multi-input multi-output (MIMO) nonlinear state systems. First, a state estimator is proposed, which is able to detect fault occurrence, by using a residual signal. Second, when the state is at an abnormal condition, the fault-tolerant control will be triggered to minimize the impact of the fault occurrence. This fault-tolerant control is designed by using a robust controller (original controller), and an on-line approximator to capture a nonlinear function that indicates the fault occurrence. The detailed analysis is given for the proposed fault accommodation control.

Abstract:This paper proposes an uncalibrated workpiece positioning method for peg-in-hole assembly of a device using an industrial robot. Depth images are used to identify and locate the workpieces when a peg-in-hole assembly task is carried out by an industrial robot in a flexible production system. First, the depth image is thresholded according to the depth data of the workpiece surface so as to filter out the background interference. Second, a series of image processing and the feature recognition algorithms are executed to extract the outer contour features and locate the center point position. This image information, fed by the vision system, will drive the robot to achieve the positioning, approximately. Finally, the Hough circle detection algorithm is used to extract the features and the relevant parameters of the circular hole where the assembly would be done, on the color image, for accurate positioning. The experimental result shows that the positioning accuracy of this method is between 0.6-1.2 mm, in the used experimental system. The entire positioning process need not require complicated calibration, and the method is highly flexible. It is suitable for the automatic assembly tasks with multi-specification or in small batches, in a flexible production system.

Abstract:In this paper, an automated system and methodology for nondestructive sorting of apples are presented. Different from the traditional manual grading method, the automated, nondestructive sorting equipment can improve the production efficiency and the grading speed and accuracy. Most popular apple quality detection and grading methods use two-dimensional (2D) machine vision detection based on a single charge-coupled device (CCD) camera detect the external quality. Our system integrates a 3D structured laser into an existing 2D sorting system, which provides the addition third dimension to detect the defects in apples by using the curvature of the structured light strips that are acquired from the optical system of the machine. The curvature of the structured light strip will show the defects in the apple surface. Other features such as color, texture, shape, size and 3D information all play key roles in determining the grade of an apple, which can be determined using a series of feature extraction methods. After feature extraction, a method based on principal component analysis (PCA) for data dimensionality reduction is applied to the system. Furthermore, a comprehensive classification method based on fuzzy neural network (FNN), which is a combination of knowledge-based and model-based method, is used in this paper as the classifier. Preliminary experiments are conducted to verity the feasibility and accuracy of the proposed sorting system.

Abstract:A distributed optical-fiber acoustic sensor is an acoustic sensor that uses the optical fiber itself as a photosensitive medium, and is based on Rayleigh backscattering in an optical fiber. The sensor is widely used in the safety monitoring of oil and gas pipelines, the classification of weak acoustic signals, defense, seismic prospecting, and other fields. In the field of seismic prospecting, distributed optical-fiber acoustic sensing (DAS) will gradually replace the use of the traditional geophone. The present paper mainly expounds the recent application of DAS, and summarizes recent research achievements of DAS in resource exploration, intrusion monitoring, pattern recognition, and other fields and various DAS system structures. It is found that the high-sensitivity and long-distance sensing capabilities of DAS play a role in the extensive monitoring applications of DAS in engineering. The future application and development of DAS technology are examined, with the hope of promoting the wider application of the DAS technology, which benefits engineering and society.

Abstract:This paper proposes a novel grading method of apples, in an automated grading device that uses convolutional neural networks to extract the size, color, texture, and roundness of an apple. The developed machine learning method uses the ability of learning representative features by means of a convolutional neural network (CNN), to determine suitable features of apples for the grading process. This information is fed into a one-to-one classifier that uses a support vector machine (SVM), instead of the softmax output layer of the CNN. In this manner, Yantai apples with similar shapes and low discrimination are graded using four different approaches. The fusion model using both CNN and SVM classifiers is much more accurate than the simple k-nearest neighbor (KNN), SVM, and CNN model when used separately for grading, and the learning ability and the generalization ability of the model is correspondingly increased by the combined method. Grading tests are carried out using the automated grading device that is developed in the present work. It is verified that the actual effect of apple grading using the combined CNN-SVM model is fast and accurate, which greatly reduces the manpower and labor costs of manual grading, and has important commercial prospects.

Abstract:An engineering system may consist of several different types of components, belonging to such physical “domains” as mechanical, electrical, fluid, and thermal. It is termed a multi-domain (or multi-physics) system. The present paper concerns the use of linear graphs (LGs) to generate a minimal model for a multi-physics system. A state-space model has to be a minimal realization. Specifically, the number of state variables in the model should be the minimum number that can completely represent the dynamic state of the system. This choice is not straightforward. Initially, state variables are assigned to all the energy-storage elements of the system. However, some of the energy storage elements may not be independent, and then some of the chosen state variables will be redundant. An approach is presented in the paper, with illustrative examples in the mixed fluid-mechanical domains, to illustrate a way to recognize dependent energy storage elements and thereby obtain a minimal state-space model. System analysis in the frequency domain is known to be more convenient than in the time domain, mainly because the relevant operations are algebraic rather than differential. For achieving this objective, the state space model has to be converted into a transfer function. The direct way is to first convert the state-space model into the input-output differential equation, and then substitute the time derivative by the Laplace variable. This approach is shown in the paper. The same result can be obtained through the transfer function linear graph (TF LG) of the system. In a multi-physics system, first the physical domains have to be converted into an equivalent single domain (preferably, the output domain of the system), when using the method of TFLG. This procedure is illustrated as well, in the present paper.

Abstract:Due to the rapid development in the petroleum industry, the leakage detection of crude oil transmission pipes has become an increasingly crucial issue. At present, oil plants at home and abroad mostly use manual inspection method for detection. This traditional method is not only inefficient but also labor-intensive. The present paper proposes a novel convolutional neural network (CNN) architecture for automatic leakage level assessment of crude oil transmission pipes. An experimental setup is developed, where a visible camera and a thermal imaging camera are used to collect image data and analyze various leakage conditions. Specifically, images are collected from various pipes with no leaking and different leaking states. Apart from images from existing pipelines, images are collected from the experimental setup with different types of joints to simulate leakage conditions in the real world. The main contributions of the present paper are, developing a convolutional neural network to classify the information in red-green-blue (RGB) and thermal images, development of the experimental setup, conducting leakage experiments, and analyzing the data using the developed approach. By successfully combining the two types of images, the proposed method is able to achieve a higher classification accuracy, compared to other methods that use RGB images or thermal images alone. Especially, compared with the method that uses thermal images only, the accuracy increases from about 91% to over 96%.

Abstract:The grading judgment for apples is related to a variety of factors including, size, shape, color, texture, and scars. Traditional manual sorting methods are time consuming and labor intensive. In addition, the accuracy of the method is easily subjective, not repeatable, error-prone, and affected by the sorting environment. This paper presents a complete and automated grading system for apples. The system uses a single-chip microcomputer as the controller of the system, and a PC as the graphics processing unit. It also includes a conveyor, drive motor, frequency converter for motor control, photoelectric sensors, air compressor, and air jets for ejecting the graded apples. The classification algorithm is implemented by using a convolutional neural network (CNN). In order to eliminate contact damage of apples, the system specifically uses air jets as actuators to eject the graded apples into the corresponding bins. At the same time, in order to ensure that an apple triggers the correct ejecting actuator, this paper designs a jet controller with proper logic.

Abstract:This paper addresses the application of quantum entanglement and cryptography for automation and control of dynamic systems. A dynamic system is a system where the rates of changes of its state variables are not negligible. Quantum entanglement is realized by the Spontaneous Parametric Down-conversion process. Two entangled autonomous systems exhibit correlated behavior without any classical communication in between them due to the quantum entanglement phenomenon. Specifically, the behavior of a system, Bob, at a distance, is correlated with a corresponding system, Alice. In an automation scenario, the “Bob Robot” is entangled with the “Alice Robot” in performing autonomous tasks without any classical connection between them. Quantum cryptography is a capability that allows guaranteed security. Such capabilities can be implemented in control of autonomous mechanical systems where, for instance, an “Alice Autonomous System” can control a “Bob Autonomous System” for applications of automation and robotics. The applications of quantum technologies to mechanical systems, at a scale larger than the atomistic scale, for control and automation, is a novel contribution of this paper. Notably, the feedback control transfer function of an integrated classical dynamic system and a quantum state is proposed.