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This book introduces readers to the principles and practical applications of intelligent robot system with robot operating system (ROS), pursuing a task-oriented and hands-on approach. Taking the conception, design, implementation, and operation of robot application systems as a typical project, and through learning-by-doing, practicing-while-learning approach, it familiarizes readers with ROS-based intelligent robot system design and development step by step. The topics covered include ROS principles, mobile robot control, Lidar, simultaneous localization and mapping (SLAM), navigation, manipulator control, image recognition, vision calibration, object grasping, vision SALM, etc., with typical practical application tasks throughout the book, which are essential to mastering development methods for intelligent robot system. Easy to follow and rich in content, the book can be used at colleges and universities as learning material and a teaching reference book for intelligent robot, autonomous intelligent system, robotics principles, and robot system application development with ROS in connection with automation, robotics engineering, artificial intelligence (AI), mechatronics, and other related majors. The book can assist in mastering the development and design of robot systems and provide the necessary theoretical and practical references to cultivate robot system development capabilities and can be used as teaching material for engineering training and competitions, or for reference, self-study, and training by engineering and technical personnel, teachers, and anyone who wants to engage in intelligent robot system development and design.

Today's professionals are constantly striving to create sensor technology and systems with lower cost and higher efficiency. Miniaturization and standardization have become critical drivers for cost reduction in the design and development process, giving rise to a new era of smart sensors and actuators. These devices contain more components, but normally provide significant cost savings due to wider applicability and mass production. This first-of-its-kind resource presents methods for cost optimization of smart microsystems to help you select highly cost-efficient implementation variants. Written by leading experts, the book offers detailed coverage of the key topics that you need to understand for your work in the field, such as methods for cost estimation, holistic design optimization, a methodology for a cost-driven design, and applied cost optimization. This practical book focuses on fundamental cost influences rather than absolute numbers, helping you appreciate relative values which reflect the competitive advantage of the various design implementations. Moreover, you find specific recommendations on which cost-reduction methods will be most advantageous in varying situations. This forward-looking volume provides keen insight into the underlying factors which drive the current economics and determine future trends of smart microsystems.

Path‐tracking and lane‐keeping efficiency of driverless cars remain critical characteristics of the efficient and safe deployment of such vehicles in future intelligent transportation systems. This study introduces a robust type‐3 (T3) fuzzy controller implementation for the path‐tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances. Unlike many existing control paradigms, the proposed scheme is independent of the parameter information and assumes the system dynamics are unknown and non‐linear. Control inputs are constructed to improve robustness by eliminating the error bounds while ensuring stability by leveraging the Lyapunov stability theorem and Barbalat's lemma. Also, a predicate scheme based on non‐linear predictive control technique is introduced to enhance the lateral displacement. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path‐tracking tasks, and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances. This study introduces a robust type‐3 fuzzy neural controller implementation for the path‐tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances.

Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

In order to enable failure mode and effects analysis (FMEA) to play a better quality control role in complex manufacturing products or systems, the current research status of FMEA is reviewed from failure mode identification, risk assessment, and industrial standard application. Firstly, the research status of system failure identification is summarized from the following aspects: the breakthrough point of identification, the types of identification methods, and the normalized description of failure modes. Then, sort out the research status of risk assessment from five aspects: risk factor evaluation criteria, risk assessment opinion expression, expert opinion consensus, risk opinion assessment aggregation, and sensitivity analysis, and find out research hotspots and blind spots; finally, the changes of FMEA standards in various fields are summarized and compared, and the future development trend of FMEA in the context of intelligent manufacturing is discussed.

Lane-keeping is a basic function of an intelligent vehicle. But the existing lane-keeping methods may not provide the expected effect. A vehicle often deviates from the desired lane despite the working lane-keeping controller in practice. For addressing this issue, we propose a novel lane-keeping control method based on the homogeneous domination control theory to improve the lane-keeping system performance in this paper. Firstly, a two-degree-of-freedom lane-keeping dynamic model is built. Then, the state equations of the lane-keeping control system are obtained based on the dynamic model. A lane-keeping state feedback controller is designed via the homogeneous domination method. We prove that the designed controller can globally asymptotically stabilize the system via the Lyapunov method. The proposed homogeneous domination method does not require the nonlinear terms of the nonlinear system to meet the strict linear growth condition. Numerical simulation and hardware-in-the-loop test results show that the proposed homogeneous controller has strong robustness, fast response, and low energy output which are more suitable for the lane-keeping system and improves the lane-keeping system performance.

With the development of 5G and Internet of Things (IoT), the era of big data‐driven product design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by recent breakthroughs in computing power and software architectures. In this regard, the digital twin, analyzing various sensor data with the help of AI algorithms, has become a cutting‐edge technology that connects the physical and virtual worlds, in which the various sensors are highly desirable to collect environmental information. However, although existing sensor technologies, including cameras, microphones, inertial measurement units, etc., are widely used as sensing elements for various applications, high‐power consumption and battery replacement of them is still a problem. Triboelectric nanogenerators (TENGs) as self‐powered sensors supply a feasible platform for realizing self‐sustainable and low‐power systems. Herein, the recent progress on TENG‐based intelligent systems, that is, wearable electronics, robot‐related systems, and smart homes, followed by prospective future development enabled by sensor fusion technology, is focused on. Finally, how to apply artificial intelligence to the design of intelligent sensor systems for the 5G and IoT era is discussed. With the development of Internet of Things (IoT) and artificial intelligence (AI), the era of big data‐driven product design is booming. Digital twin, analyzing various sensor data with the help of AI, has become a cutting‐edge technology. Recent progress on triboelectric nanogenerator (TENG) provides a new possibility for realizing self‐sustainable systems integrated with digital twin.

Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed.

In order to provide intelligent services, the Internet of Things (IoT) facilitates millions of smart cyber‐physical devices to be enabled with network connectivity to sense, collect, process, and exchange information. Unfortunately, the traditional communication infrastructure is vulnerable to cyber attacks and link failures, so it is a challenging task for the IoT to explore these applications. In order to begin research and contribute into the IoT‐based cyber‐physical digital world, one will need to know the technical challenges and research opportunities. In this study, several key technical challenges and requirements for the IoT communication systems are identified. Basically, privacy, security, intelligent sensors/actuators design, low cost and complexity, universal antenna design, and friendly smart cyber‐physical system design are the main challenges for the IoT implementation. Finally, the authors present a diverse set of cyber‐physical communication system challenges such as practical implementation, distributed state estimation, real‐time data collection, and system identification, which are the major issues require to be addressed in implementing an efficient and effective IoT communication system.

Automated Guided Vehicles (AGVs) are essential elements of manufacturing intralogistics and material handling. Improving the position accuracy along the AGV trajectory allows the vehicle to work on narrower aisles with lower error tolerance. Despite the increasing number of papers in AGVs and mobile robots’ position control research area, there is a lack of curatorial work presenting and analyzing the control strategies applied in the problem domain. Therefore, the main objective is to analyze the published researches of the past seven years on the position control of AGVs to recognize research patterns, gaps, and tendencies, outlining the research field. The paper proposes a systematic literature review to investigate the research field from the controller design perspective. Its protocol and procedures are presented in detail. Four main research topics were addressed: the control strategies used in the AGV position control problem, how the literature presents the AGV operating requirement of position accuracy, how the literature validate the proposed controller and present their results regarding the system’s position accuracy, and the technological tendencies the proposed solutions reveals. Besides, within the main topics, other points were investigated, such as the AGV application area, the considered mathematical model, the sensors and guidance system used, and the maximum payload of the vehicle and operation under different load conditions. The data synthesis shows the predominant control strategies applied to the problem and the interaction among distinct control theory areas, indicating a notable interaction of Intelligent Control techniques to the other strategies. The paper’s contributions are using a systematic literature review method over the AGV position control publications, presenting an overview of the research area, analyzing the research question topics from selected articles, and proposing a research agenda.