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Condition monitoring and fault diagnosis in modern manufacturing automation is of great practical significance. It improves quality and productivity, and prevents damage to machinery. In general, this practice consists of two parts: 1) extracting appropriate features from sensor signals and 2) recognizing possible faulty patterns from the features. Through introducing the concept of marginal energy in signal processing, a new feature representation is developed in this paper. In order to cope with the complex manufacturing operations, three approaches are proposed to develop a feasible system for online applications. This paper develops intelligent learning algorithms using hidden Markov models and the newly developed support vector techniques to model manufacturing operations. The algorithms have been coded in modular architecture and hierarchical architecture for the recognition of multiple faulty conditions. We define a novel similarity measure criterion for the comparison of signal patterns which will be incorporated into a novel condition monitoring system. The sensor-based intelligent system has been implemented in stamping operations as an example. We demonstrate that the proposed method is substantially more effective than the previous approaches. Its unique features benefit various real-world manufacturing automation engineering, and it has great potential for shop floor applications. Note to Practitioners—This paper was motivated by the problem of detecting the complicated sheet-metal processes faults in a high success rate but also applies to the real-world practices. Existing systems adopt traditional approaches fast but cannot adapt to various cases and achieve a diagnosis purpose. Using hidden Markov models and the newly developed support vector techniques, this paper proposes three approaches and develops intelligent learning algorithms to model manufacturing operations. The ovel similarity measure criterion is able to implement the signal patterns comparison, overcoming the time-shifting phenomenon. The approach of modular architecture and hierarchical architecture has been presented in the software coding for the recognition of multiple faulty conditions. The sensor-based hardware design facilitates the online condition monitoring system. In this paper, we mathematically characterize the process signal features. We then develop the HMM-based modular fault diagnosis system which makes the modification expansion easy in practice. The hierarchical SVM-based fault diagnosis system uses a small-size training sample but achieves a high successful rate, and overcomes the overfitting and difficulties in structure design of the learning machine. The similarity measure approach using support vector techniques requires training sample sets produced under normal operating conditions and, thus, makes it more feasible than other techniques. Meanwhile, this approach overcomes the time-shifting. The proposed system provides important technologies and valuable reference for research areas and an innovation to manufacturing automation. The system works well in most applications; however, in some cases, certain parameters have to be tuned manually, for example, the parts of the signals to be modeled. The next step is to develop the system so it can learn human skills and set parameters automatically. In the short term, it is important to research ways to improve the system's intelligent online learning. [ABSTRACT FROM AUTHOR]

In practice, the environments in which mobile robots operate are usually modeled in highly complex forms and, as a result, autonomous navigation and localization can be difficult. The difficulties are exacerbated for practical robots with limited on-board computational resources and complex planning algorithms, since this paradigm of environmental modeling requires enormous computational power. A novel navigation/localization learning methodology is presented to abstract and transfer the human sequential navigational skill to a robotic wheelchair by showing the platform how to respond in different local environments along a demonstrated, static cluttered route using a lookup table representation. This method utilizes limited on-board range sensing information to concisely model local unstructured environments, with respect to the robot, for navigation or localization along the learned route in order to achieve good performance with low on-line computational demand and low-cost hardware requirements. Experimental study demonstrates the feasibility of this method and some interesting characteristics of navigation, localization, and environmental modeling problems. Analysis is also conducted to investigate performance evaluation, advantages of the approach, choices of lookup table inputs and outputs, and potential generalization of this paper. [ABSTRACT FROM AUTHOR]

Examines the criteria for the task of human driving. Use of real-time graphic driving simulator; Identification of control strategy model; Aspects of human control strategy models.

Describes the Gyrover single-wheel, gyroscopically stabilized robot. Application of the concept of dynamic mobility; Configuration; Advantages over multiwheel, statistically stable vehicles; Design prototypes; System implementation; Nonholonomic constraints; Stabilizing effects of the flywheel on the system.

Studies the problem of modeling and control of multiple cooperative manipulators handling a rigid object. Holonomic property of multiple manipulators; Development of a trajectory tracking controller that yields asymptotic convergence of position errors and bounded interaction forces; Kinematic and dynamic modeling of multiple manipulators.

This paper studies the problem of modeling and control of multiple cooperative underactuated manipulators handling a rigid object. We reveal holonomic property of such a system by presenting a smooth feedback controller subject to two conditions: 1) there are not fewer active joints than the degrees of freedom of the object; and 2) the Jacobian matrix with respect to passive joints is not singular. This controller is an extension of the PD plus gravity compensation scheme and its asymptotic stability is guaranteed by the LaSalle theorem. Furthermore, we develop a trajectory tracking controller that yields asymptotic convergence of position errors and bounded interaction forces simultaneously. The performance of the proposed controllers has been investigated by simulations on two 6-DOF underactuated manipulators and by experiments on the cooperative underactuated manipulator system developed at CMU. [ABSTRACT FROM PUBLISHER]

This article presents some results of an ongoing research project to investigate the components and modules that are necessary to equip a robot with exploratory capabilities. Of particular interest is the recovery of certain material properties from a surface, given minimal a priori information, with the intent to use this information to enable a robot to stand and walk stably on a surface that is unknown and unconstrained. To this end, exploratory procedures (EPs) have been designed and implemented to recover penetrability, compliance, and surface roughness by exploring the surface using a compliant wrist sensor. A six-degree-of-freedom compliant wrist sensor that combines passive compliance and active sensing has been developed to provide the necessary flexibility for force and contact control, as well as accurate position control. This article describes the compliant wrist and sensing mechanism design along with a hybrid control algorithm that utilizes the sensed information from the wrist to adjust the apparent stiffness of the end effector as desired. [ABSTRACT FROM AUTHOR]

In this article inhomogeneously of magnetized ferrite disks are studied by two different methods: the division of the disk into thin rings with field matching at their boundaries, and the solution of differential equations of inhomogeneously magnetized ferrites. Results obtained from these two methods are in good agreement with each other. Circulation conditions of inhomogeneously magnetized planar ferrite junction circulators are then analyzed, and the effects of inhomogeneity of magnetization on these conditions are examined. [ABSTRACT FROM AUTHOR]

Modeling dynamic human control strategy (HCS), or human skill in response to real-time sensing is becoming an increasingly popular paradigm in many different research areas. We propose a stochastic similarity measure, based on hidden Markov model analysis, capable of comparing and contrasting stochastic, dynamic, multidimensional trajectories. We first derive and demonstrate properties of the similarity measure for stochastic systems. We then apply the similarity measure to real-time human driving data by comparing different control strategies among different individuals. We show that the proposed similarity measure out performs the more traditional Bayes classifier in correctly grouping driving data from the same individual. Finally, we illustrate how the similarity measure can be used in the validation of models which are learned from experimental data, and how we can connect model validation and model learning to iteratively improve our models of HCS. [ABSTRACT FROM PUBLISHER]

In this article the characterization of planar structures using the method of lines in cylindrical coordinates is presented. The capacitance of different segments of a microstrip circular ring is studied; special attention is given to edge stray capacitances. This method of calculation can find applications in the characterization of microstrip components in MIC, MHMIC, and MMIC with shapes easily fitted to cylindrical coordinates (e.g., circular ring, fan shape, etc.). [ABSTRACT FROM AUTHOR]

We are developing a novel robot concept called the wearable robot. Wearable robots are mobile information devices capable of supporting remote communication and intelligent interaction between networked entities. In this paper, we explore the possible functions of such a robotic network and will present a distributed network architecture based on service components. In order to support the interaction and communication between the components in the wearable robot system, we have developed an intelligent network architecture. This service-based architecture involves three major mechanisms. The first mechanism involves the use of a task coordinator service such that the execution of the services can be managed using a priority queue. The second mechanism enables the system to automatically push the required service proxy to the client intelligently based on certain system- related conditions. In the third mechanism, we allow the system to automatically deliver services based on contextual information. Using a fuzzy-logic-based decision making system, the matching service can detennine whether the service should be automatically delivered utilizing the information provided by the service, client, lookup service, and context sensors. An application scenario has been implemented to demonstrate the feasibility of this distributed service-based robot architecture. The architecture is implemented as extensions to the Jini network model. [ABSTRACT FROM AUTHOR]

A dual neural network is presented for the bi-criteria kinematic control of redundant manipulators. To diminish the discontinuity of minimum infinity-norm solutions, the kinematiccontrol problem is formulated in the bi-criteria of the infinity and Euclidean norms. Physical constraints such as joint limits and joint velocity limits are also incorporated simultaneously into the proposed kinematic control scheme. The single-layer dual neural network model with a simple structure is developed for bi-criteria redundant resolution of redundant manipulators subject to robot physical constraints. The dual neural network is shown to be globally convergent to optimal solutions in the bi-criteria sense, and is demonstrated to be effective in controlling the PAl0 robot manipulator. [ABSTRACT FROM AUTHOR]

With the substantial increase of vehicles on road, driving safety and transportation efficiency have become increasingly concerned focus from drivers, passengers, and governments. Wireless networks constructed by vehicles and infrastructures provide abundant information to share for the sake of both enhanced safety and network efficiency. This paper presents the systematic research to enhance the vehicle safety by wireless communication, in the aspects of information acquisition through vehicle sensing, vehicle-to-vehicle (V2V) routing protocol for the highly dynamic vehicle network, vehicle-to-infrastructure (V2I) routing protocol for a tradeoff in real-time performance and load balance, and hardware implementation of V2V system with on-road test. Simulations and experimental result validate the feasibility of the algorithms and communication system. [ABSTRACT FROM AUTHOR]

Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method. [ABSTRACT FROM AUTHOR]

State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studies in simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties. [ABSTRACT FROM AUTHOR]

The technologies of autonomous rendezvous and robotic capturing of non-cooperative targets are very crucial for the future on-orbital service. In this paper, we proposed a method to achieve this aim. Three problems were addressed: the target recognition and pose (position and attitude) measurement based on the stereo vision, the guidance, navigation and control (GNC) of the chaser, and the coordinated plan and control of space robot (CP&C). The pose measurement algorithm includes image filtering, edge detection, line extraction, stereo match and pose computing, et al. Based on the measured values, a certain GNC algorithm was designed for the chaser to approach and rendezvous with the target. Then the CP&C algorithm, which is proved to be advantageous over the traditional separated method, was used to plan and track the trajectories of the base pose and the joint angle. At last, a 3D simulation system was developed to evaluate the proposed method. Simulation results verified the corresponding algorithms. [ABSTRACT FROM AUTHOR]

Space robotic systems are expected to play an increasingly important role in the future. Unlike on the earth, space operations require the ability to work in the unstructured environment. Some autonomous behaviors are necessary to perform complex and difficult tasks in space. This level of autonomy relies not only on vision, force, torque, and tactile sensors, but also the advanced planning and decision capabilities. In this paper, the authors study the autonomous target capturing from the issues of theory and experiments. Firstly, we deduce the kinematic and dynamic equations of space robotic system. Secondly, the visual measurement model of hand-eye camera is created, and the image processing algorithms to extract the target features are introduced. Thirdly, we propose an autonomous trajectory planning method, directly using the 2D image features. The method predicts the target motion, plans the end-effector's velocities and solves the inverse kinematic equations using practical approach to avoid the dynamic singularities. At last, numeric simulation and experiment results are given. The ground experiment system is set up based on the concept of dynamic simulation and kinematic equivalence. With the system, the experiments of autonomous capturing a target by a free-floating space robot, composed of a 6-DOF manipulator and a satellite as its base, are conducted, and the results validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the non-holonomic characteristic of a free-floating space robotic system is used to plan the path of the manipulator joints, by whose motion the base attitude and the manipulator joints attain the desired states. Here, we parameterize the joint trajectory using sinusoidal functions, whose arguments are high-order polynomials. Then, we define the cost function for optimization according to the constraint conditions and the accuracy of the space robot. Finally, genetic algorithms (GAs) are used to search for the solutions of the parameters. Compared with others, our approach has advantages as follows. (i) The motion of the manipulator and the disturbance on the base are practically constrained. (ii) The dynamic singularities cannot affect the algorithm since only the direct kinematic equations are utilized. (iii) The planned path is smooth and more applicable for the control of the manipulator. (iv) The convergence of the algorithm is not affected by the attitude singularity since the orientation error is represented by quaternion, which is globally singularity-free. The simulation results of the spacecraft with a 6-d.o.f. manipulator verify the performance and the validity of the proposed method. [ABSTRACT FROM AUTHOR]