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.

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.

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

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 Q_c 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.

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

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.

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.

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.

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.

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.