Synthesis of product-driven coordination controllers for a class of discrete-event manufacturing systems

A synthesis approach is proposed for discrete-event coordination architectures applied to a class of automated manufacturing systems (AMS) in which a clear separation is established between equipment control activities and product manufacturing procedures. Manufacturing procedures are modeled by regular languages constructed with a class of control commands named imperative. Equipment controllers are synthesized as a standard discrete-event supervisors dealing only with operational and safety issues of equipment groupings. The control of equipment modules is carried out following the imperative control commands sequences. Conditions are established to guarantee that the manufacturing procedure of a given product can be achieved using the synthesized supervisors in a particular AMS. Therefore, equipment controllers are not needed to be modified to consider the manufacturing of different products, whilst the construction of achievable manufacturing procedures becomes an “ad hoc” simple process using a reduced set of procedural blocks. The approach is illustrated with an experimental AMS.     

面向智能制造的工业机器人健康评估方法

以智能制造领域中最具有代表性的设备——工业机器人——为研究对象,针对其精度退化及设备故障问题,研究了工业机器人健康评估问题．首先,对工业机器人核心部件进行了失效模式及影响分析,并对现有工业机器人健康评估方法进行了综述．其次,提出了基于边－云协同和深度学习的工业机器人健康评估框架．在边缘层应用基于群组聚类和对等比较的方法进行异常检测,快速识别出发生异常的设备;在云端借助故障预测与健康管理技术以及人工智能算法对发生异常的设备进行深度健康评估．最后,对基于深度学习的工业机器人健康评估方法进行了展望．     

An integrated control strategy for simultaneous robot assignment,tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network

There has been a leap in the field of smart manufacturing with the advancement of automation systems, robotic technology, big data analytics, and state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Three very important aspects of smart manufacturing systems are system productivity, product quality, and maintenance of machines and equipment. These three issues are strongly interrelated and collectively determine the performance of a smart manufacturing system. Although there has been significant studies in production control, quality control and maintenance scheduling to address each of these aspects individually, there has been a lack of sufficient studies taking all of them into consideration in one control scheme. In this paper, a mobile multi-skilled robot operated Flexible Manufacturing System (FMS) is considered and a model that integrates robots, individual workstation processes and product quality is developed using a Heterogeneous Graph Structure. Heterogeneous Graph Neural Network (HGNN) is used to aggregate local information from different nodes of the graph model to create node embeddings that represent global information. A control problem is then formulated in the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework to simultaneously consider robot assignment, product quality and maintenance scheduling. The problem is solved using Multi-Agent Reinforcement Learning (MARL). A case study is presented to demonstrate the effectiveness of the HGNN-MARL control strategy by comparing it to three baselines and the naive MARL policy without HGNN.     

Improved control and simulation models of a tricycle collaborative robot

The objective of collaborative manufacturing is to create the synergism from the collaboration of manufacturing resources. Most of the studied collaborations are made among intelligent machines; however, the collaboration can be realized even between machines and human being, and a collaborative robot (Cobot) belongs to the latter. A cobot is a robot designed to assist human beings as a guide or assistor in a constrained motion. Various prototypes have been developed and the potentials of these robots have been demonstrated. The research presented in this paper focuses on the control and simulation models of a tricycle cobot with three steered wheels, with the following two contributions: (i) A concise model for the closed-loop control is developed. Existing closed-loop control has been implemented in an intuitive way, and some control parameters have to be determined by a trial-and-error method. (ii) A simulation model is proposed to validate the control algorithms. No simulation model is available and the control models of other existing systems have to be validated experimentally. The developed control and simulation models have been implemented. Graphic simulation is also developed. Case studies are provided and the simulation results are analyzed.     

Contribution to the indirect decentralized adaptive control of manipulation robots

In this paper, efficient approaches to the synthesis of indirect decentralized adaptive control for manipulation robots are presented. The first part of control synthesis consists of the estimation of unknown dynamic robot parameters using the methods of recursive identification and fast dynamic as well as identification models in a symbolic form. The second part of synthesis includes the self-tuning control strategy which is a basis for adaptive control synthesis according to the estimates of the unknown dynamic parameters. Using the theory of decentralized systems, a new robust algorithm for adaptive control with the ability of adaptation in the feedforward or feedback loop are proposed. A complete stability and convergence analysis is presented. A special part of the paper represents an analysis of practical implementation of the proposed control algorithms on modern microprocessor-based robot controllers. Based on this analysis, an efficient application of indirect adaptive algorithms in real time with high-quality system performance is shown. Adaptive algorithms are verified through simulation of trajectory tracking for an industrial robot with unknown dynamic parameters of payload.     

Modular manufacturing

This paper discusses requirements to be satisfied by future manufacturing systems and proposes a new concept of modular manufacturing to integrate intelligent and complex machines. In large-scale systems such as manufacturing systems, modularization is indispensable for clarifying logical structure and assuring a high degree of ease of construction. The parts, products and manufacturing equipments as well as the design and operating activities themselves are all described in units called modules. A manufacturing system is constructed and operated by combining these in building-block style. The creation of this manufacturing system relies on construction and operating systems that enable design and simulation in the virtual world, and production and control in the real world, in a unified approach. Hardware modules and software modules are compiled flexibly and hierarchically to fulfil specified tasks. A system in which modular manufacturing as a concept of system integration is applied to manufacturing robots is called a modular robot system. The robots are embedded in manufacturing systems as the highest application of model-based robotics.     

Adaptive control for manufacturing systems using infinitesimalperturbation analysis

Combining infinitesimal perturbation analysis (IPA) with stochastic approximation gives identification algorithms to estimate the optimal threshold value for failure-prone manufacturing systems consisting of one machine producing one part type. Two adaptive control schemes are proposed. The adaptive control schemes do not require the knowledge of the distribution functions of the up and down times. Under some appropriate conditions, the strong consistency, as well as the convergence rates, of the identification algorithms and the cost function is established for the adaptive control schemes. In particular, it is shown that central limit theorems hold for the identification algorithms     

Adaptive exponential stabilization of mobile robots with unknown constant‐input disturbance

This paper concentrates on the discussions on stabilization of mobile robots with unknown constant‐input disturbance. Continuous time‐varying adaptive controllers are designed for mobile robots in a chain‐form by using Lyapunov approach. With the property of homogeneous systems, uncertain mobile robots governed by the proposed control algorithms become homogeneous of order 0 to achieve exponential stability. Simulation results validate the theoretical analysis. © 2001 John Wiley & Sons, Inc.     

Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.     

Educational training software for modelling and synthesis of controllers for robotic manipulators and robotized manufacturing cells

A general software system aimed at computer-aided design of controllers for robots and robotized technological systems is described in this paper. The software system includes modules for the synthesis of various levels of robot controller as well as controllers of complex robotized technological systems. The software includes simulation of robotic systems within manufacturing cells using various types of models: complete dynamic models, kinematic models and simple models in the form of finite automata. Using these modelsvarious algorithms for all controls levels in robot controllers may be synthesized taking into account the actual interaction between the robot and its environment. The software system enables the solution of the important problem of the interaction between higher and lower levels of controllers. Finally, a general purpose controller as a target system for the proposed software is described. The controller is designed as an open system allowing the user to apply various control laws and to run in conjunction with an actual robot. The general software system together with the controller represents a powerful educational tool in modern robotics.     

Holonic coordination obtained by joining the contract net protocol with constraint satisfaction

Present manufacturing systems are facing significant challenges concerning their adaptability. Holonic manufacturing systems are among the technologies that can provide solutions to such requests, if certain conditions are met. As being included in the class of semi-heterarchical control architectures, holonic systems need appropriate coordination and planning schemes, together with validation tools to increase the beneficiaries’ trust. This paper proposes a coordination scheme for holonic systems, based on a mix between contract net protocol and distributed constraint satisfaction problems. The distinct phases of this method are explained, with details referring to the adaptation of contract net protocol. About the constraint satisfaction mechanism, a guide is provided on how a manufacturing problem can be expressed according to this formalism. To validate the introduced coordination scheme, a coloured Petri net model was developed. This allowed several simulation experiments for scenarios regarding a manufacturing system with four robots to be carried out. The obtained solutions showed that the proposed method can determine both the right holarchy related to the manufacturing goal, and optimal plans for robots. Moreover, as proven by the reachability graphs obtained for different goals, the proposed method reached correct results for all goals and diverse constraints, and it determined all possible solutions. An advantage is about how knowledge possessed by different types of holons is efficiently used, without producing an increased communication load. In conclusion, our method can ensure the right trade-off between complexity and optimality, and the attached model can constitute the required link between design and implementation, thus contributing to an easier deployment of holonic systems.     

Rapid response manufacturing through a rapidly reconfigurable robotic workcell

This article describes the development of a component-based technology robot workcell that can be rapidly configured to perform a specific manufacturing task. The workcell is conceived with standard and inter-operable components including actuator modules, rigid link connectors and tools that can be assembled into robots with arbitrary geometry and degrees of freedom. The reconfigurable “plug-and-play” robot kinematic and dynamic modeling algorithms are developed. These algorithms are the basis for the control and simulation of reconfigurable robots. The concept of robot configuration optimization is introduced for the effective use of the rapidly reconfigurable robots. Control and communications of the workcell components are facilitated by a workcell-wide TCP/IP network and device-level CAN-bus networks. An object-oriented simulation and visualization software for the reconfigurable robot is developed based on Windows NT. Prototypes of the robot workcells configured to perform the light-machining task and the positioning task are constructed and demonstrated.     

A Proposed Hybrid Recurrent Neural Control System for Two Co-operating Robots

This paper discusses a model refernce adaptive (MRAC) position/force controller using proposed neural networks for two co-operating planar robots. The proposed neural network is a recurrent hybrid network. The recurrent networks have feedback connections and thus an inherent memory for dynamics, which makes them suitable for representing dynamic systems. A feature of the networks adopted is their hybrid hidden layer, which includes both linear and nonlinear neurons. On the other hand, the results of the case of a single robot under position control alone are presented for comparison. The results presented show the superior ability of the proposed neural network based model reference adaptive control scheme at adapting to changes in the dynamics parameters of robots.     

Expert system to match robots and to synchronize their operations to pick and place large parts

The operation of pick and place large parts is a very common operation on the manufacturing floor. The efficiency of the pick and place operation affects the efficiency of the entire manufacturing floor. Large parts in terms of this research are parts which must be transported by two robots.This paper concludes the development of an expert system and a numerical simulation to assign two available robots to perform the pick and place operations of large parts.The developed expert system consists of a knowledge-base structured in interrelated frames to define large manufacturing systems and an inference-engine to perform the robots matching and their performances monitoring. Matching robots considerations can not be performed by numerical algorithms in real time systems, because they are major computation time consumers. Therefore, unmatched robots can be assigned by the numerical algorithms to perform inefficient pick and place operations. The expert system and the numerical simulation were programmed, and many computer runs were performed to explore the efficiency of the two systems in a large variety of manufacturing systems.The achieved results show that the expert system has no advantage in assigning matched robots to perform the pick and place operations in systems characterized by very short machine process times (0.5 moment to 1 moment). If such short machine process times are relevant, the knowledge-base is updated very frequently, and the inference-engine reasoning processes can not be initiated. For longer machine process times, the frequency of knowledge-base changes is reduced, and the inference-engine reasoning processes can be initiated without delays. Therefore, the advantages of the expert system become significant if machine process times are extended.     

Safety assurance mechanisms of collaborative robotic systems in manufacturing

Collaborative robots (cobots) are robots that are designed to collaborate with humans in an open workspace. In contrast to industrial robots in an enclosed environment, cobots need additional mechanisms to assure humans’ safety in collaborations. It is especially true when a cobot is used in manufacturing environment; since the workload or moving mass is usually large enough to hurt human when a contact occurs. In this article, we are interested in understanding the existing studies on cobots, and especially, the safety requirements, and the methods and challenges of safety assurance. The state of the art of safety assurance of cobots is discussed at the aspects of key functional requirements (FRs), collaboration variants, standardizations, and safety mechanisms. The identified technological bottlenecks are (1) acquiring, processing, and fusing diversified data for risk classification, (2) effectively updating the control to avoid any interference in a real-time mode, (3) developing new technologies for the improvement of HMI performances, especially, workloads and speeds, and (4) reducing the overall cost of safety assurance features. To promote cobots in manufacturing applications, the future researches are expected for (1) the systematic theory and methods to design and build cobots with the integration of ergonomic structures, sensing, real-time controls, and human-robot interfaces, (2) intuitive programming, task-driven programming, and skill-based programming which incorporate the risk management and the evaluations of biomechanical load and stopping distance, and (3) advanced instrumentations and algorithms for effective sensing, processing, and fusing of diversified data, and machine learning for high-level complexity and uncertainty. The needs of the safety assurance of integrated robotic systems are specially discussed with two development examples.     

Digital twin-based industrial cloud robotics: Framework,control approach and implementation

Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.     

Genetic synthesis of production-control systems for unreliable manufacturing systems with variable demands

The control of manufacturing systems with variable demands has attracted much research attention over the years. However, only limited results have been obtained due to the difficulty of this production-control problem. In this paper, genetically optimized short-run hedging points are used to construct gain-scheduled adaptive controllers for unreliable manufacturing systems with variable demands. The performance of such adaptive controllers is illustrated for unreliable systems subjected to piecewise-constant demands. It is demonstrated that the performance of these adaptive controllers is superior, in general, to that of genetically optimized non-adaptive controllers. However, such gain-scheduled adaptive controllers are designed for variable demands that are piecewise-constant. Therefore, in order to deal with more general classes of variable demands, a genetic rule-induction design methodology is used to synthesize robust fuzzy-logic controllers to provide automatic closed-loop control for unreliable manufacturing systems. Such robust fuzzy-logic controllers are shown to provide effective control for unreliable manufacturing systems with various kinds of variable demands.     

A design of bilateral teleoperation systems using composite adaptive controller

This paper presents a synchronization scheme of bilateral teleoperation systems using composite adaptive controller. To design a controller for bilateral teleoperation systems, all the parameters of the master and the slave robots need to be known. However, there exist parameter uncertainties in the robot manipulators. A composite adaptive controller is designed for convergence of states and parameters of the master and the slave robots in the presence of parameter uncertainties. Consequently, position and force tracking problems in free and contact motion are solved in a synchronized manner. Through a number of simulations, the superiority of the proposed method over existing works is illustrated. Furthermore, for the validation of utility of the proposed method in an actual embedded system, the algorithms are implemented and tested in FPGA-based hardware controller.     

High-fidelity simulation of integrated single-wafer processing tools for evaluation of scheduling algorithms

Semiconductor manufacturing equipments are being integrated into complex systems that perform multiple processes in a single contained unit. An integrated single-wafer processing tool, composed of multiple single-wafer processing modules and transfer robots, has complex re-visit routing sequences, and often has critical post-processing residency constraints at the process modules. The simulation of the single-wafer processing tools presented in this paper is to test and validate on-line schedulers, and evaluate the performance of the integrated single-wafer processing tools before they are actually deployed into the fabs. The developed simulator consists of six components which are a graphic user interface, an emulator, an execution system, a manager, an analyzer and a 3D animator. The overall framework is built by using Microsoft Visual C++, and the animator is constructed by using OpenGL. The emulator has the state models of the process and transfer modules, and control functions that execute unit processes of the transfer robots. The manager checks the states of the robots, and sequentially calls these control functions to fulfill transfer commands. The execution system automatically generates contingencies with pre-defined failure lists, and determines whether the rest of the operable wafers should be further processed or discarded. The animator shows real-time 3D animation of the operation of the processing tools. The analyzer provides various performance measures such as throughput rate, cycle time, utility, and ratio of overtime to residency (ROR). Users can test and evaluate various manufacturing scenarios and configurations of the processing tools and recipes.