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Industry 4.0 is a strategic initiative introduced by the German government during early 2010s to transform industrial manufacturing through digitalisation and exploitation of the potentials of new technologies. It is an effort to increase productivity and efficiency mainly in the manufacturing sector. Industry 4.0 production system aims to be highly flexible and should be able to produce individualised and customised products. In fact, it is an exciting employment of automation within manufacturing, covering the use of robotics, data management, cloud computing and the intemet of things (IoT). It has started to show that artificial intelligence, robotics, smart sensors and integrated systems are an important part of a normal manufacturing process. In interaction with machines, it needs horizontal integration at every step in the production process. The Americans have the same concept for Industry 4.0 but prefer to call it Smart Factory. The nine pillars of Industry 4.0 transforms isolated cell production into a fully optimised, integrated and automated production flow.
For many years, industrial robots have been utilised to change mass manufacturing and register automation to carry out unique procedures more quickly and safely without any human lapse. The mass manufacturing produced from industrial robots lowers the cost of the product and increases the speed of delivery without much error. Production procedures, technology development, smart supply chain and trade aspects have been changed by Industry 4.0. Artificial intelligence (Al) and machine learning transformed the older ways of executing processes and operating industrial robots. To this end, novel research has been carried out and plans have been made for future robot generation, automation lines and smart factories. Although Industry 4.0 is not a widely used term and known idea, it is outstandingly capable of enhancing human life. All manufacturing procedures and supply levels are prognosticated to be impacted in the production. A relation of Industry 4.0 and smart factories in terms of industrial robots is described in this chapter and the benefits and applications are addressed. The influence of industrial robots on smart factories and the future expectations are later discussed in the text.
Industry 4.0 refers to automation and data exchange in manufacturing technologies. From innovative research, challenges, solutions and strategies to real-world case studies, the aim of this edited book is to focus on the nine pillars of technology that are supporting the transition to Industry 4.0 and smart manufacturing. The nine pillars include the internet of things, cloud computing, autonomous and robotics systems, big data analytics, augmented reality, cyber security, simulation, system integration, and additive manufacturing. A key role is played by the industrial IoTs and state-of-the-art technologies such as fog and edge computing, advanced data analytics, innovative data exchange models, artificial intelligence, machine learning, mobile and network technologies, robotics and sensors. This book is a useful resource for an audience of academic and industry researchers and engineers, as well as advanced students in the fields of information and communication technologies, robotics and automation, big data analytics and data mining, machine learning, artificial intelligence, AR/VR/ER, cybersecurity, cyber physical systems, sensing and robotics with a focus on Industry 4.0, and smart manufacturing.
This study addresses the platoon formation control problem of multiple off-axle hitching tractor–trailers with limited communication ranges, under model uncertainties and external disturbances, without any collision and without velocity and acceleration measurements for the first time. Towards this end, a new second-order Euler–Lagrange formulation of tractor–trailers is introduced under the prescribed performance design procedure that preserves all structural properties of the tractor–trailer dynamics. Then, a prescribed performance non-linear transformation, a saturated filtered tracking error, radial basis function neural networks, an adaptive robust controller, and a high-gain observer are creatively employed to design a novel platoon output-feedback controller, which forces the vehicles to construct a desired convoy while guaranteeing the robust performance against unmodelled dynamics and external forces and ensuring inter-vehicular communication maintenance, collision avoidance between each successive pair in the convoy of vehicles, and some preassigned desired response specifications of platoon formation errors including overshoot/undershoot, convergence speed, and ultimate tracking accuracy. By utilising a Lyapunov-based stability analysis, a semi-global uniform ultimate boundedness of formation errors is ensured with prescribed performance. Finally, simulation results illustrate the efficacy of the proposed control system.
The full-field pulse-echo ultrasonic propagation imaging (PE UPI) system using a two-axis linear stage has been successfully used to perform a non-destructive evaluation of flat specimens. This study used a six-axis robot arm to develop a robotic scanning algorithm that can be applied to the laser pulse-echo inspection of non-flat objects. The robotic PE UPI system was constructed and verified for both flat and curved specimens. The internal structures and defects of a honeycomb sandwich plate and a glass-fibre-reinforced polymer specimen were visualised by the robotic PE UPI system. The quality of the evaluation of the flat specimen was equivalent to that of a two-axis linear system, and the result of the curved specimen showed improvement compared to that of the linear system. The ability of the robotic scanning technique to evaluate the structural health performance of arbitrarily shaped surfaces was validated.
In this chapter a reconfigurable trajectory -tracking control design method has been presented for autonomous in-wheel electric vehicles with independently controlled hub motors and the steer-by-wire steering system. The high-level control reconfiguration has been implemented through the design of a scheduling variable using the LPV framework in order to deal with fault events, while in normal operating conditions the objective of the reconfiguration is to maximize battery SOC, thus enhancing the range of the in-wheel electric vehicle. The energy optimal control reconfiguration has been designed based on the results of preliminary simulations with a high-fidelity vehicle and electrical models on different road conditions. Finally, the efficiency of the proposed method has been demonstrated in a real-data CarSim simulation, showing significant energy saving by the proposed method
The aim of this book was to provide possible solutions to the problem of fault diagnosis and fault-tolerant control (FTC) of robotic and autonomous systems. A total of 44 international authors contributed to the book in the form of 12 chapters, describing both theoretical findings and challenging applications.
Robotic systems in the modern industries are working in increasingly complex environment. The unexpected external disturbances and the abrupt change of working condition bring great challenges to the control of such systems. For some safetycritical applications, it is required to continuously stabilize the systems under faulty condition (including malfunctions and system failures). In this chapter, a data-driven fault-tolerant control (FTC) framework is introduced. As a semi-supervised machine learning technique that can learn from the rewards from external environment, reinforcement learning (RL) aims to maximize the long-term returns by, for instance, maintaining a value function. Based on the approximated value function, optimal control law can be derived. When using RL, although the convergence of the value function has been proved for some simple systems, such as the linear time invariant system, the internal stability of the close-loop system still remains unguaranteed. To deal with this problem, a novel framework for FTC system design based on Youla parameterization is introduced. It can be implemented in the modular and plug-and play manner. It should be noted that in this work RL acts as a supervising module that calculates optimized Youla matrix Qc in the design phase. Simulation results on a wheeled robot are provided to show the performance of the proposed approach in the continuously stabilizing framework. Some open questions and future work are summarized at the end of this chapter.
For most industrial robots using position control, self-collision is a headache. Without well-defined constraints at a joint level, self-collision can lead to damage to a robot or even injury to the operator. In this study, a new methodological approach is proposed that is different from the traditional methods of using sensors or analysing kinematics. The proposed method uses a support vector machine to construct a joint boundary map based on the robot's movement in the workspace. To verify its feasibility and performance, simulations are conducted. The proposed method is believed to help reduce the cost and computational burden. It is expected to be utilised even in applications where the existing methods are difficult to use.
Subsurface networks include mining tunnels, caves, and the urban underground. Each of these environments presents a complex setting with significant challenges in exploration, exploitation, etc. Multiple hazards exist, including environmental and structural, and conditions degrade and change temporally. We present a survey of research in autonomy, networking and mobility focused on exploring and/or mapping subsurface networks in unpredictable and unexplored environments. The focus is on mining tunnels as a proxy subterranean environment; motivated by the proximity of the authors to the Bonita Peak Mining District; a Superfund site consisting of 48 historic mines which have contaminated soil, groundwater and surface water with heavy metals as a result of historic practices. The exploration and assessment of the mining tunnels for remediation efforts presents an extremely challenging problem in subterranean navigation. Arguably, the environment in question is the most extreme and challenging subterranean environment. Unmanned assets must enter to explore and re-map the tunnels to assess safety for subsequent entrance by robots and/or humans for proper remediation. The survey is split into three categories -- Locomotion, GPS-denied navigation and localization, and Communication. It is concluded with a proposed design for a platform that addresses the difficulties of exploring an abandoned mine
2020 is set to see history made with the first ever servicing operation on an in-orbit satellite. Robotic orbital troubleshooters, which are expected to pave the way for a new era of space robotics, will one day shuttle malfunctioning spacecraft back and forth, refuel them, perform basic repairs or serve as temporary propulsion and steering units. The present paper looks at Space Logistics' MEV-1 (Mission Extension Vehicle-1) in particular, together with its mission to rendezvous with Intelsat IS901 and boost it into an orbit which will enable it to operate for a further five years. After five years, Intelsat IS901 will then be sent into a graveyard orbit and MEV-1 will move on to another satellite. Also mentioned is the work being carried out by Effective Space to carry out servicing and other life-extension services.
Industrial robots, as a structurally sophisticated mechatronics system, have a high cost of routine maintenance and repair. Repairs after fault require the corresponding manpower and material resources, and have hysteresis. If the fault can be predicted in a timely and accurate manner, the maintenance process can be carried out in advance, and the hidden dangers can be eliminated to fundamentally solve the fault problem. Based on the self-organised critical theory (SOC theory), this article draws lessons from its self-organisation evolution model and uses the self-organised criticality of industrial robot fault to establish an autoregressive moving average model (ARMA model) for industrial robots. According to the analysis of residual value and the explanation for the faults of industrial robots, find ways and means to prevent and reduce faults.
Maintenance robot is used widely in industries due to its convenience. The maintenance robot system is introduced. The hardware frame is designed in detail. Based on gesture recognition, the effect of controlling the robot movement is achieved. Gesture recognition information is achieved through the acquisition of human skeleton information by a Kinect sensor. The construction of the gesture library is done. The maintenance robot control system is set up to control the omnidirectional mobile robot driven by the Mecanum wheel. Finally, the maintenance robot is developed and the design functions are carried out.
Here, a robust adaptive trajectory tracking algorithm is proposed for free-form surface grinding robot (FSGR) in metal surface production line. Machine-learning method is used for robot dynamic approximation which is hard to obtain directly. Adaptive law is proposed to adjust the neural network parameters. Sliding-mode control is employed to deal with the disturbance, joint friction, and approximation error of the adaptive machine learning. The scheme based on machine-learning feedforward compensation can significantly reduce the chattering of sliding mode. The performance of the proposed control scheme is illustrated through simulations.
The sine eccentric shaft is one of the most important parts for industrial robot RV reducer, and its machining quality has immediate effect on the overall device performance. The eccentric shaft X-C linkage grinding model is built based on the tangential point tracing machining principle, and the method of calculating the roundness error for eccentric circle grinding is proposed here. The research is performed on non-uniform rational B-spline (NURBS) interpolation for the coordinates’ densification of eccentric shaft X-C linkage grinding, and the algorithm process is put forward. The process of eccentric grinding is simulated and analysed by wheel inversion envelope method. The results show that the eccentric tangent point tracking model proposed is correct. Compared with the linear interpolation, NURBS interpolation can remarkably improve the accuracy of eccentric shaft tangent point tracking grinding.
At present, the box type cargo picking robot mostly adopts the sucker type end-effector in the logistics industry in china. In the light of the disadvantages and limitation of the complex heavy pneumatic devices used in the existing sucker type end-effector, a new type of clamping end-effector's structure is designed here. Relying on the clamp, flip, clamping to realise the pickup of the box goods. Only by stepping motor as the driving device, the weight of the driving system is greatly reduced, so that the overall structure of the robot is lightweight and the selection efficiency is improved. The finite element analysis of key components improves the feasibility of structural design. The finite element analysis of key components verifies the feasibility of the structure design and provides references for subsequent optimisation.
Workflow compositions have been exploited in business process modelling to handle concurrent invocations of modular components. With the emergence of Industry 4.0 warehouse automation, which enable the integration of business processes, mechanised robots, sensor–actuators and human participants, analysis and specification of workflows become crucial. As such environments have dynamic deployments due to varying demand rates and environmental conditions, the workflow compositions are intended to be adaptable to runtime changes. In addition, monitoring the end-to-end latency and optimal runtime binding is critical in industrial deployments such as warehouse automation. The authors provide specifications in the concurrent programming language Orc that supports most commonly used workflow patterns. Complex deployments involving multiple robotic agents and business processes further require analysis of correctness, liveness, and safety properties. In order to verify the workflows, the Orc specifications are translated into workflow net representations, with verification done using the TAPAAL model checker. The advantages of deploying fine grained analysis of workflows are demonstrated over picker/delivery robots involved in warehouse operations. The envisioned set of reusable specifications may be extended and applied to a variety of Industry 4.0 deployments to handle complex workflow interactions.
The collaboration between a human driver and an automation system will serve as an effective measure before the autonomous driving technology is fully implemented. To discuss the driving authority between a human driver and an automation system, a novel moving horizon shared steering framework is proposed, in which the controller assists the driver when the vehicle is in danger. First, a potential-hazard analysis is presented based on the potential steering operation error and lane departure distance to predict the degree of a hazard and to determine the driving authority between the human driver and the automation system. Then, the minimum collaborative steering operation is determined using a moving horizon optimisation approach with safety constraints. Thus, the automation system shares steering with the driver protect the vehicle from risks in a non-invasive manner. To demonstrate the effectiveness of the proposed strategy, simulations with different types of drivers and scenarios are conducted. The results of these simulations demonstrate that the proposed approach can enhance the driving ability of drivers with different skill levels in dangerous situations. The approach can also guarantee the safety of the intelligent vehicle to some extent when drivers lose focus for a period of time.
Even by providing benefits addressing the main megatrend in today's manufacturing industries human–robot interaction (HRI) still lacks reasonably applied use cases, especially in small and medium-sized enterprises (SMEs). Most SMEs worry about complicated standards they have to consider when letting their employees work with a robot. Furthermore, they simply do not know where to start due to multiple promised benefits not precisely matching their requirements. Since they have individual starting points regarding their needs, available process data and documentation, it is expensive to obtain a customised evaluation of provided benefits. Thereby, the main challenge is to offer a consistent approach dealing with multiple requirements. The multi-layer concept of service modelling provides a possibility to resolve this contradiction. It aims for an overall goal by achieving predefined subgoals with individually matching methods. Thus, each manufacturing company can choose the most suitable methodology to analyse its production line. Regarding HRI it is possible to select workstations with the best fitting solution according to individual requirements. The selection will be quantified based on analyses about provided values of HRI in manufacturing. This study presents the service modelling approach for the evaluation of workstations that are best suited for the implementation of HRI in SME.
The article discusses the law relating to new technologies. Areas covered include: a licence for science; a law for space exploitation; a law for smart machines; a law to enforce truth online and a law for reusable technology.