A particular concern with SAE Level 3 automation is the takeover transition from the automated vehicle to the human driver. In response, research has focused on investigating this transition. However, researchers have used a wide range of metrics to measure takeover performance. The lack of consistency in these metrics poses challenges for synthesizing findings. To address this issue, we conducted a systematic literature review of studies published between January 2009 and December 2019, focusing on the takeover performance metrics. Following prior research, we categorize these metrics into two dimensions: timeliness and quality. Additionally, we summarize the scenarios used to elicit takeover requests and analyze the corresponding maneuvers (braking, lane changing, and lane keeping). The results have shown inconsistencies in calculation and naming conventions of takeover performance metrics. Based on these findings, this study proposes several directions for standardizing definitions and terminology, and advancing toward a unified measure of takeover performance.

Supply chain (SC) risk management is influenced by both spatial and temporal attributes of different entities (suppliers, retailers, and customers). Each entity has given capacity and lead time to process and transport products to downstream entities. In disruptive events, lead times and capacities may vary, which affects the overall performance of SC. There have been many studies on SC disruption mitigation, but often without considering lead time and the magnitude of lateness. In this paper, we formulate a mixed integer programming (MIP) model to optimize SC operations via a routing and scheduling approach, to model the delivery time of products at different entities as they flow throughout the SC network. We minimize a weighted sum of multiple objectives that involve costs related to transportation, shortages, and delivery lateness. We further develop a Benders decomposition algorithm for speeding up the computation of the NP-hard MIP model. We also develop a discrete-event simulation framework to evaluate the performance of solutions to the MIP model under lead time uncertainty. Through extensive numerical studies, we show how the attributes of SC entities affect the performance, so that we can improve the SC design and operations under various uncertainties.

After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies comparing different formats of AARs have focused mainly on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, and designed a 1$\times 3$between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects' SA over the other conditions.

When humans supervise multiple semi-autonomous robots while also attending to their own tasks simultaneously, they may lack the situation awareness needed to assist their robot teammates. There is a need to monitor the human’s situation awareness in real-time, so interventions can be taken to improve poor situation awareness. While prior work has developed models to estimate human situation awareness, they rely heavily on advanced machine learning models and a single source of input through eye-tracking that can pose operational challenges. We develop a real-time human situation awareness estimator based on data from a human-robot teaming experiment. The situation awareness estimator uses simple and interpretable logistic regression models that take inputs from both eye-tracking and behavioral measures. Cross-validation demonstrated the situation awareness estimator had an average accuracy of 74%. The estimator is robust to missing inputs, and can monitor human situation awareness non-intrusively in real-time.

Effective human supervision of autonomous robots in high-stakes scenarios requires efficient intervention, particularly when unmanned ground vehicles (UGVs) encounter local minima problems. This study investigates user interface designs to support human intervention in resolving such issues without a complete system takeover. We conducted a human-subjects experiment comparing two intervention methods: direct waypoint selection via mouse input and directional commands via arrow keys. Participants supervised two UGVs while simultaneously performing a secondary task, simulating real-world multitasking scenarios. Results demonstrate that mouse-based waypoint selection led to significantly more efficient UGV paths than arrow key controls and was also preferred by participants. Our findings contribute to the design of human-autonomy interfaces.

Modern supply chain networks (SCN) are becoming increasingly complex, with vulnerable entities exposed to uncertain disruptions that affect local or global supply chain attributes. We model a stochastic mixed-integer program to minimize the overall cost of SCN design and operations, in response to lead-time and demand uncertainties following given probability distributions. We formulate a heterogeneous risk-aware model to trade off between cost and delay/shortage by considering different risk-attitudes amongst supply chain agents. In particular, we employ the Conditional Value-at-Risk (CVaR) as a coherent risk measure for quantifying risk while attaining solution tractability. We derive managerial insights from our numerical studies, finding the most benefit from diversifying agents in the root tier, since their disruptions affect all other tiers in the SCN. We find that as agents become more risk averse, the optimal solutions for key agents (such as assemblers), seek more backup suppliers and allocate extra capacities to achieve resiliency and reliability. Practitioners can use the outcomes of our framework and studies to guide SCN design considering heterogeneous risk attitudes between agents. Note to Practitioners—With growing uncertainties in global supply chains, inefficient responses to disruptions can lead to large penalties and long-term impacts such as customer dissatisfaction. This research is motivated by the challenges arising during the operations of supply chains under both lead-time and demand uncertainties. We employ optimization and centralized control approaches to optimize supply-chain network design as well as response strategies to disruptions, and our framework can handle heterogeneous risk preferences as it models the risk attitude of each individual entity or agent in supply chains. Our model can be utilized to completely or partially re-design resilient supply chains, to better prepare for unknown features and uncertainties. Our case study provides insights about risk-averse supply-chain designs that can reduce response cost, but increase initial investments on backups and redundancies.

The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types.

The capability of robots to rapidly adapt to new tasks without extensive reprogramming offers significant flexibility in reconfiguration of manufacturing processes to cope with unforeseen events. In modern manufacturing environments where numerous hardware and software systems exchange data with each other to perform a myriad of tasks, modularizing sub-systems and reusing commonly available information like product CAD models can increase robustness and efficiency of the reconfiguration. Yet, current approaches for robotic grasping tend to focus on standalone vision-based learning that often require either retraining to adapt to new object categories or massive dataset not available in manufacturing environments, making generalization challenging. This paper addresses the problem of exploiting available information, like CAD models, in manufacturing settings to efficiently generate a tractable set of grasps for known rigid objects, which can be directly applied to a wide class of robotic manipulations. In order to quickly produce diverse grasp configurations for arbitrary geometric models, we present GraspMixer, a combination of (1) an efficient offline sampler that utilizes specifications of a parallel-jaw gripper, and (2) a mapping function that fuses multiple features of a grasp to output a binary quality metric. During evaluation using physics-based simulations, a robotic gripper successfully executes 92.9% of all grasp configurations for 12 novel objects selected by GraspMixer. Among five different grasp sampling methods, GraspMixer also achieves the highest grasp success rate when performing table-top single object grasping under object pose uncertainty. The computation of this offline pipeline takes less than 1.0 minutes for each object without GPU hardware acceleration, which is comparable to or outperforms most of the benchmarks in the evaluation. Importantly, our framework exhibits impressive simulation-to-reality adaptation, achieving over 95% grasp success rate on previously unseen novel objects. All of these results are achieved with fewer than 10% of the samples typically used by other learning-based grasping techniques. Note to Practitioners—Modularization is a major theme in current manufacturing systems to increase efficiency. In this work, we introduce a new framework called GraspMixer, which is part of a larger manipulation and decision making architecture to enable versatile robotic manipulation in a manufacturing environment. The framework decomposes the task of reasoning about graspable local surfaces on object 3D models into sequentially connected sub-components. GraspMixer leverages information about objects and grippers, including their 3D models, materials, and inertial properties, which are available in a manufacturing environment. This enables our framework to automatically precompute grasping points on new objects that can be shared among multiple robots equipped with parallel-jaw grippers. GraspMixer synergizes with Internet of Things (IoT) and Cloud Computing platforms to efficiently scale up advanced robotic automation in manufacturing. Such a combination could provide greater flexibility in deploying advanced perception systems in a manufacturing environment to accelerate adaptation of the automation while saving computational resources of onboard processors within robots.

Robotic manipulation of deformable linear objects (DLOs) is an active area of research, though emerging applications, like automotive wire harness installation, introduce constraints that have not been considered in prior work. Confined workspaces and limited visibility complicate prior assumptions of multi-robot manipulation and direct measurement of DLO configuration (state). This work focuses on single-arm manipulation of stiff DLOs (StDLOs) connected to form a DLO network (DLON), for which the measurements (output) are the endpoint poses of the DLON, which are subject to unknown dynamics during manipulation. To demonstrate feasibility of output-based control without state estimation, direct input-output dynamics are shown to exist by training neural network models on simulated trajectories. Output dynamics are then approximated with polynomials and found to contain well-known rigid body dynamics terms. A composite model consisting of a rigid body model and an online data-driven residual is developed, which predicts output dynamics more accurately than either model alone, and without prior experience with the system. An adaptive model predictive controller is developed with the composite model for DLON manipulation, which completes DLON installation tasks, both in simulation and with a physical automotive wire harness.

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach.Note to Practitioners—This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

In recent years, the frequent occurrence of disruptions has had a negative impact on global supply chains. To stay competitive, enterprises strive to remain agile through the implementation of efficient and effective decision-making strategies in reaction to disruptions. A significant effort has been made to develop these agile disruption mitigation approaches, leveraging both centralized and distributed decision-making strategies. Though trade-offs of centralized and distributed approaches have been analyzed in existing studies, no related work has been found on understanding supply chain performance based on the networkattributesof the disrupted supply chain entities. In this paper, we characterize supply chains from a capability and network topological perspective and investigate the use of a distributed decision-making approach based on classical multi-agent frameworks. The performance of the distributed framework is evaluated through a comprehensive case study that investigates the performance of the supply chain as a function of the network structure and agent attributes within the network in the presence of a disruption. Comparison to a centralized decision-making approach highlights trade-offs between performance, computation time, and network communication based on the decision-making strategy and network architecture. Practitioners can use the outcomes of our studies to design response strategies based on agent capabilities, network attributes, and desired supply chain performance.Note to Practitioners—This research is motivated by the challenges in determining agile decision-making strategies that enable a supply chain enterprise to adapt to disruptions while taking into account the network-based attributes of the disrupted agent and the requirements of the supply chain system. Existing approaches in the literature focus on providing one feasible decision-making strategy based on specific performance metrics. This paper investigates both centralized and distributed approaches to better understand the differences between the response strategies in the case of supplier loss. More specifically, we design a supply chain instance and conduct a case study to evaluate the performance of the centralized and distributed approaches in terms of several common performance metrics used in practice. The case study provides insights for users to select a decision-making approach based on the network attributes and agent capabilities of the supply chain. The impact of network uncertainties and risk assessment are not considered in this work. Future studies will investigate a stochastic supply chain environment and heterogeneous risk management framework in the context of agile decision-making for disrupted supply chain enterprises.

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems. Note to Practitioners—An intelligent product is a product in a manufacturing system that is able to make decisions based on a set of specifications and affect its own production process. Intelligent products have often been proposed to address the challenges associated with small-batch manufacturing and highly customized production. Specifically, by using intelligent products, manufacturers would be able to complete small orders without the need to reconfigure or reschedule operations in the manufacturing system. However, one of the major challenges in the implementation of this control strategy is the need to develop methods that allow intelligent products to cooperate with machines, robots, and other products in a manufacturing system. In this work, we propose a novel direct and active cooperation framework that allows intelligent products to communicate and cooperate with other resources and products on the shop floor. Using the proposed cooperation framework, intelligent products can resolve scheduling conflicts and work together to meet individual specifications (e.g., deadlines). Two case studies, a small job shop and a large semiconductor manufacturing system, showcase how the proposed cooperation framework can be leveraged for different types of applications.

In conditionally automated driving, in-vehicle alert systems can provide drivers with information to assist their takeovers from automated driving. This study investigated how display modality and information influenced drivers’ acceptance of the in-vehicle alert systems under different event criticality situations. We conducted an online video study with a 3 (information type) × 3 (display modality) × 2 (event criticality) mixed design involving 60 participants. The results showed that considering drivers’ perceived usefulness and ease of use, presenting why only information was not sufficient for takeovers as compared to what will only information and why + what will information. Participants reported higher ease of use in the combination of speech and augmented reality condition when compared to the speech only condition. High event criticality led to drivers’ lower perceived usefulness and more negative opinions of the displays. The findings have implications for the design of in-vehicle alert systems during takeover transitions.

In conditionally automated driving, drivers engaged in non-driving related tasks (NDRTs) have difficulty taking over control of the vehicle when requested. This study aimed to examine the relationships between takeover performance and drivers’ cognitive load, takeover request (TOR) lead time, and traffic density. We conducted a driving simulation experiment with 80 participants, where they experienced 8 takeover events. For each takeover event, drivers’ subjective ratings of takeover readiness, objective measures of takeover timing and quality, and NDRT performance were collected. Results showed that drivers had lower takeover readiness and worse performance when they were in high cognitive load, short TOR lead time, and heavy oncoming traffic density conditions. Interestingly, if drivers had low cognitive load, they paid more attention to driving environments and responded more quickly to takeover requests in high oncoming traffic conditions. The results have implications for the design of in-vehicle alert systems to help improve takeover performance.

For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians' trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians' gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further, we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.

Cyber-physical manufacturing systems (CPMS) can be defined by the integration of control, network communication, and computing with a physical manufacturing process. In this work, we present a hybrid model of CPMS combining sensor data, context information, and expert knowledge. We used the identification of global operational states and a multimodel framework to improve anomaly detection and diagnosis. The anomaly detection is based on context-sensitive adaptive threshold limits. Root cause diagnosis is based on classification models and expert knowledge. The proposed approach was implemented using the Internet of Things (IoT) to extract data from a computer numerical control machine. Results showed that using a context-sensitive modeling strategy allowed to combine physics-based and data-driven models for residual analysis to detect an anomaly in the part, machine, or process. The identification of root cause was improved by adding context information in classification models to identify worn or broken tools and wrong material.

In current practice, product developers with customized small batch production needs come across the problem of finding capable and flexible manufacturers, whereas manufacturers face underutilization due to inconsistent demand. This paper presents a Production as a Service (PaaS) framework to connect users (consumers or product developers) who have customized small batch manufacturing needs with manufacturers who have existing underutilized resources. PaaS is a cloud-based, centralized framework based on a service-oriented architecture that abstracts the manufacturing steps of a product as individual (production) service requests. Using PaaS, the user is able to reach many capable manufacturers at once and receive quotations for the production request. On the other end, PaaS reduces the effort required to find new customers and enables the manufacturers to easily submit quotations to increase the utilization of their resources. The functionalities and concepts defined in this paper are illustrated through case studies. Note to Practitioners-In order to transition product fabrication from the design phase to manufacturing, there is a need for identifying capable manufacturers with available resources that could be paired with user requirements. We propose Production as a Service (PaaS) in this paper and present initial implementations of the front-end and back-end components with a customizable optimization algorithm. Proposed abstractions and data structures make PaaS a unique, efficient, and intellectual property preserving framework. The current implementation of the framework has been tested with new designs. The PaaS framework has the potential to scale over a large network of manufacturers to effectively coordinate geo-distributed manufacturers for custom manufacturing needs.

This paper introduces a framework to assess the performance of manufacturing systems using hybrid simulation in real time. Continuous and discrete variables of different machines are monitored to analyze performance using a virtual environment running synchronous to plant floor equipment as a reference. Data are extracted from machines using industrial Internet of Things solutions. Productivity and reliability of a physical system are compared in real time with data from a hybrid simulation. The simulation uses discrete-event systems to estimate performance metrics at a system level, and continuous dynamics at a machine level to monitor input and output variables. Simulation outputs are used as a reference to detect abnormal conditions based on deviations of real outputs in different stages of the process. This monitoring method is implemented in a fully automated manufacturing system testbed with robots and CNC machines. Machines are integrated on an Ethernet/IP control network using a programmable logic controller to coordinate actions and transfer data. Results demonstrated the capacity to perform real-time monitoring and capture performance errors within confidence intervals. Note to Practitioners-Estimating expected performance of a manufacturing system processing different parts across multiple machines is a complex problem due to the lack of closed-form equations. Existing solutions focus on monitoring stochastic variables such as production or failure rate, or machine dynamics in separate environments often running asynchronous to the real system. This paper addresses the problem of monitoring and assessing the performance of complex manufacturing systems in real time. The proposed framework uses a real-time hybrid simulation of manufacturing at a machine and system level. The hybrid approach is based on a discrete and continuous model of manufacturing equipment integrated to run synchronously with the real plant floor operation. Data from both the virtual and real environments are merged to assess performance. Deviations from expected values represent an error that can trigger a warning signal to production, maintenance, and/or manufacturing personnel at the plant regarding health and productivity of plant operations.

Present-day manufacturing companies encounter a variety of challenges due to the dynamically changing industrial environment. Current control frameworks lack the adaptability and flexibility to effectively deal with challenges such as broken-down machines or altered customer orders. Multi-agent control has been proposed to improve the performance of manufacturing systems in uncertain or dynamic environments. Some multiagent architectures have been introduced with promising results. A key component of these architectures is the product agent, which is responsible for guiding a physical part through the manufacturing system based on the production requirements of the part. Even though the product agent has been previously used in multi-agent frameworks, a well-defined internal architecture for this agent has yet to be proposed. This work specifies a product agent architecture that can be utilized in multi-agent systems. The proposed architecture is tested using a manufacturing system simulation. The simulation results showcase the reactivity, proactiveness, and autonomy of the proposed product agent.

The papers in this special section are devoted to the topic of human-centered automation. The central theme of these papers are the tools and methods for the design and analysis of human-centered automation systems including: the design and validation of computational models of systems that integrate models of the human with models of autonomous and semi-autonomous systems; the design of systems that ease the transfer of information between humans and autonomous systems; the analysis and prediction of potential conflicts between the human and the automation in semi-autonomous systems; the design of autonomy to accommodate varying levels of human experience, training, and acuity; the analysis of information asymmetry in collaborative, semi-autonomous systems; the design of autonomy for off-nominal conditions, such as multiple sensor failures, human error, or other cascading events; and the design of autonomy to support systems with multiple humans; the design of autonomous systems which are “self-aware,” so that humans are prompted to intervene when necessary.

Standard modeling and evaluation methods have been classically used in analyzing engineering dynamical systems where the fundamental problem is to minimize the (mean) error between the real and predicted systems. Although these methods have been applied to multi-step ahead predictions of physiological signals, it is often more important to predict clinically relevant events than just to match these signals. Adverse clinical events, which occur after a physiological signal breaches a clinically defined critical threshold, are a popular class of such events. This paper presents a framework for multi-step ahead predictions of critical levels of abnormality in physiological signals. First, a performance metric is presented for evaluating multi-step ahead predictions. Then, this metric is used to identify personalized models optimized with respect to predictions of critical levels of abnormality. To address the paucity of adverse events, weighted support vector machines and cost-sensitive learning are used to optimize the proposed framework with respect to statistical metrics that can take into account the relative rarity of such events.

This paper explores the impact of communication latency (time delay) on path following for a teleoperated mobile robot. In [1] Vozar developed a human steering model capturing the effects of latency on teleoperated driving performance and introduced the idea of equating path following performances under variable latency to an equivalent constant latency. We repeat Vozar's user experiments on a higher fidelity (dynamic) simulation using different latency distributions. Two key findings are presented. First, user tests support a relationship between path following scores for latency distributions of different shapes. Second, our user tests with the dynamic simulation produced the same trend describing latency's impact on performance as Vozar's results with a kinematic simulation.

Humans are an integral part of many tasks performed by mobile robots and human operator ability can vary greatly between users. Understanding operator skill level can allow for user interfaces to adapt giving novice users additional assistance and expert users more freedom. In this paper, a method is proposed for predicting a human teleoperator's performance based on user behavior observed during the first few minutes of teleoperation. Preliminary analysis indicates a trend between operator performance and the proportion of time an operator spends thinking during a particular segment of time.

Verifying that logic control satisfies some desirable properties is essential to the proper and safe functioning of a manufacturing system. Input order robustness is one such property that has not been thoroughly explored. If a logic controller is input order robust for all sets of inputs whose elements can arrive in any order and whose order should not affect the logic controller's final state or set of outputs, then the logic controller behaves the same in these regards irrespective of the inputs' order. This paper develops a procedure to verify input order robustness for logic controllers implemented in a variety of formalisms, and demonstrates its application on Event-Condition-Action Modular Finite-State Machines (ECA MFSM) and IEC 61499 controllers. Additionally, this verification is extended to a class of networks of controllers, and the computational complexity of such verifications are discussed.

Hardware-in-the-loop (HIL) is a widely used testing approach for embedded systems, where real components and/or controllers are tested in closed-loop with a simulation model. In this paper, we generalize HIL by combining multiple simulations and real components into a Hybrid Process Simulation (HPS). An HPS is a test setup that contains at least one simulated and one actual component, but may contain many of both. It is implemented such that each simulated component can be swapped out with its real counterpart without making changes to the existing system, and vice versa. In this paper, an ontology which provides a conceptual architecture is developed for an HPS, such that a general interpretation of a manufacturing system's implementation is made possible. A formalized application method is then devised for replacing simulations with real processes and vice versa. A conceptual architecture is put forth that separates the effect of a component from its spatial essence (volume or mass). This separation allows workpieces in a manufacturing process, for example, to go from the physical world into the virtual world (computer simulation) and back again repeatedly. The conceptual architecture is applied to a small manufacturing line in the following scenarios: replacing a real robot with a simulated robot, replacing a manufacturing cell with a simulated manufacturing cell, and adding a new simulated manufacturing cell to the existing system. These applications successfully demonstrate how an HPS can be used to test a manufacturing system setup with multiple regions of real and simulated components.

Detecting and debugging faults more efficiently can significantly improve the performance of systems, and a first step toward fault detection is anomaly detection. A new anomaly detection solution is proposed in this paper for event-based systems that consist of processes that interact through shared resources and that do not have a preexisting formal discrete event system model. This solution generates models of the system, assesses the models' performance in detecting faults, and then uses the models and their performance to detect anomalies in new event streams. A new resource-based Petri net formalism is introduced to model these types of systems. The model generation uses an algorithm based on workflow mining to generate resource-based models. The proposed solution is demonstrated on two manufacturing cell examples.

The objective of this paper is twofold. First, a new model of team optimization is formulated. Second, this model is used to investigate the effects of uncertainty on interaction. A model of team optimization that encompasses the classical team decision problem is introduced. This model is suitable for problems where agents' posterior information is not shared and is possibly inconsistent with the mutual prior information. For a broad class of problems, every agent's dominant beliefs about the posterior information of the other agents are derived. Then, the level of interaction and the level of uncertainty are defined, and the relationship between these two levels is studied. It is shown that the optimal level of interaction decreases as the level of uncertainty increases, and in some cases, the optimal level of interaction tends to zero, suggesting that the optimization problem may be decomposed. The theoretical results are demonstrated on sensor network examples.

The most notable trend in manufacturing over the past five years is probably the move towards networks at all levels. At lower levels in the factory infrastructure, networks provide higher reliability, visibility, and diagnosability, and enable capabilities such as distributed control, diagnostics, safety, and device interoperability. At higher levels, networks can leverage internet services to enable factory-wide automated scheduling, control, and diagnostics; improve data storage and visibility; and open the door to e-manufacturing. This paper explores current trends in the use of networks for distributed, multilevel control, diagnostics, and safety. Network performance characteristics such as delay, delay variability, and determinism are evaluated in the context of networked control applications. This paper also discusses future networking trends in each of these categories and describes the actual application of all three categories of networks on a reconfigurable factory testbed (RFT) at the University of Michigan. Control, diagnostics, and safety systems are all enabled in the RFT utilizing multitier networked technology including DeviceNet, PROFIBUS, OPC, wired and wireless Ethernet, and SafetyBUS p. This paper concludes with a discussion of trends in industrial networking, including the move to wireless for all categories, and the issues that must be addressed to realize these trends

The contribution of this paper is the introduction of the event-condition-action (ECA) paradigm for the design of modular logic controllers that are reconfigurable. ECA rules have been used extensively to specify the behavior of active database and expert systems and are recognized as a highly reconfigurable tool to design reactive behavior. This paper develops a method to design modular logic controllers whose dynamics are governed by ECA rules, with the ultimate goal of producing reconfigurable control. Modularity, integrability, and diagnosability measures that have in the past been used to measure the reconfigurability of manufacturing systems are used to assess the reconfigurability of the developed controllers. For the modularity measure, criteria found in computer science to evaluate the modularity of object-oriented programs are adapted to evaluate the modularity of modular logic controllers. The results of this paper are that reconfigurability is highly dependent on the level of modularity of the logic control system, and that not all "modular" structures are reconfigurable. There are approaches, such as the one shown in this paper using ECA rules, that can greatly increase the modularity, integrability, and diagnosability of the logic control system, thus increasing its reconfigurability. Note to Practitioners-This paper has been motivated by the problem of designing reconfigurable modular logic controllers. Reconfiguration is important in manufacturing, but it has also been an issue in the software design domain. There are software systems that currently exist, such as active data bases or expert systems with very powerful reconfiguration capabilities enabled by event-condition-action (ECA) rules. This paper applies the ECA concept to the design of modular logic controllers. This paper begins by describing what an ECA logic system is and then focuses on how ECA logic systems can be implemented with modular control approaches. To this end, two designs are considered. First, modular finite state machines are used to construct ECA logic systems, and a theoretical framework is built using this approach. Three qualitative measures for reconfigurability (modularity, integrability, and diagnosability) are presented and the controllers are evaluated using these measures. Second, an implementation using the IEC 61499 function block standard is presented as it is a widely understood and accepted standard for modular control applications. Future work entails theoretical analysis using modular verification techniques that exploit a controller structure

This correspondence presents the application of an existing deadlock-free resource allocation control method for a reconfigurable manufacturing system (RMS) with serial and parallel configuration, and further proposes a new higher level deadlock avoidance control method. RMSs have been introduced to replace traditional large-volume production systems such as dedicated manufacturing systems, adding more flexibility and convertibility. Such manufacturing systems require that their controls be changed rapidly to cope with unpredictable market demands; their desired control behaviors must also be verified in advance of running the system to reduce the ramp-up time. One important desired control behavior is the deadlock freeness in the resource allocation. A rule-based matrix method that has been proposed to develop deadlock-free resource allocation control is applied to an example RMS with serial and parallel configuration. Through this application, higher level deadlocks were found in the example RMS that are not prevented with the existing method. A new control method to avoid the higher level deadlocks is developed.

This paper presents one approach to design logic control systems using event-condition-action (ECA) rules implemented with IEC 61499 function blocks (FBs). ECA rules have been used extensively to specify the behavior of active database and expert systems and are recognized as a highly reconfigurable tool to design reactive behavior. Recent results have shown how the use of ECA rules can greatly increase the modularity, integrability, and diagnosability of the logic control system thus increasing its reconfigurability. This paper proposes a FB architecture such that the behavior of the control system follows the ECA paradigm.

PROFINET is the industrial Ethernet standard devised by PROFIBUS International for "Ethernet on the plant floor". PROFINET allows to implement a comprehensive communications solution on Ethernet which includes peer-to-peer communication between controllers, distributed I/O, machine safety, motion control and data acquisition. In this paper an analysis is conducted on the peer-to-peer interlocking performance based on PROFINET specification. Tests were performed to determine the performance of the peer-to-peer communication mechanism, to evaluate the impact of switches on the system, and to measure the impact of data size on peer-to-peer communication performance. The paper summarizes the test results.

The improvement in Ethernet based technologies has resulted in its use in real-time applications on the factory floor. Manufacturers, vendors and end users of automation devices are aiming at the economical and technical benefits of Ethernet based communication. Many Ethernet based protocols have been developed due to the promotion by several companies and organizations. There has been a considerable amount of research on whether these Ethernet based systems are able to fulfill the real-time requirements. To this end, this paper presents an evaluation of EtherNet/IP and its applicability for peer-to-peer communications on the factory floor.

The choice of implementing a safety system on a dedicated network or on a shared network (with a control system) rests solely on the system cost involved. To determine which option leads to a lower overall system cost, a two-tiered normalized weighted cost calculator approach that evaluates the trade-offs is presented. Applying the calculator to the decision process shows that either solution could be determined to be optimal, depending on the weights applied to specific cost factors as a result of the application environment.

When sensory and actuation devices in a control system are exchanging data through one common communication medium, the sharing of communication bandwidth will induce unavoidable data latency and might degrade the control performance. Hence, the utilization of communication resource and the requirement of control specification should be analyzed and properly designed when implementing a control system over a network architecture. In this paper, we analyze the performance of information sharing of multiple cooperative agents over one communication network, and propose design methodologies of guaranteeing acceptable control and communication performance in a networked control system. In particular, we study the relationship between the sampling rates of a control system,and the transmission rates of a communication network, and then utilize an integrated networked control design chart to help select design parameters and visualize overall system performance at different sampling and transmission rates. Based on the design parameters selected, the communication modules by utilizing deadband control and state estimation are presented for guaranteeing both control and communication performance. Simulation studies are conducted in a network-and-control simulation tool that is developed on the Matlab/Simulink platform and is used to demonstrate the proposed design methodologies. Both the analysis and simulation results illustrate the characteristics of designing mechanisms between control and communication performance and show the improvement of implementing the proposed communication modules.

A static control system architecture cannot meet the demand for continually introducing new parts into manufacturing systems while maintaining high quality and throughput for existing parts; a reconfigurable control infrastructure is needed. Control authority in the factory is typically allocated to three levels: the machine, cell, and system. The machine controllers must be reconfigurable to process different parts, the cell controllers must be reconfigurable to adapt to different part flows, and the system level controller must be reconfigurable to adapt to new parts in the system. In this paper, we describe the reconfigurable control infrastructure that we have developed at the University of Michigan's reconfigurable factory testbed. We then show through example how this infrastructure enables the real-time reconfiguration of the controllers at the machine and cell levels to adapt to quality problems and unexpected faults

The main purpose of this paper is to model and analyze a reconfigurable manufacturing system (RMS) applying existing supervisory controller design and analysis methods. The RMS introduced in this paper was originally developed to replace traditional large-volume production lines, allowing more flexibility. In this paper, the modelling for supervisory controllers and the analysis of the deadlock for RMS are performed with two existing supervisory controller design and analysis methods. One method is based on the Petri net (PN) synthesis method using resource control nets (RCN). The other is a rule-based matrix formalism constructed with traditional industrial engineering tools. A typical example of RMS with serial and parallel part-flow is presented. Also, the applicability of each method for RMS is investigated.

In this paper we present a method of comparing logic control design methodologies very early in their development, before integrated development environments are complete and extensive user testing can be performed. We base this comparison on a framework of the process that is needed to create and debug logic. This comparison method provides objective measures that can be used to compare existing industrial logic control design methodologies (such as ladder diagrams and flow charts) with more recently developed academic methods (such as Petri nets or state machines). We demonstrate this comparison by comparing ladder diagrams, Petri nets and modular finite state machines.

Policy-based management provides a means for IT systems to operate according to business needs. Unfortunately, there is often an "impedance mismatch" between the policies administrators want and the controls they are given. Consider the Apache Web server. Administrators want to control CPU and memory utilizations, but this must be done indirectly by manipulating tuning parameters such as MaxClients and KeepAlive. There has been much interest in using feedback control to bridge the impedance mismatch. However, these efforts have focused on a single metric that is manipulated by a single control and hence have not considered interactions between controls such as those that are common in computing systems. This paper shows how multiple-input, multiple-output (MIMO) control theory can be used to enforce policies for interrelated metrics. MIMO is used both to model the target system, Apache in our case, and to design feedback controllers. The MIMO model captures the interactions between KA and MC, and can be used to identify infeasible metric policies. In addition, MIMO control techniques can provide considerable benefit in handling trade-offs between speed of metric convergence and sensitivity to random fluctuations while enforcing the desired policies.

A widely used approach to achieving service level objectives for a software system (e.g., an email server) is to add a controller that manipulates the target system's tuning parameters. We describe a methodology for designing such controllers for software systems that builds on classical control theory. The classical approach proceeds in two steps: system identification and controller design. In system identification, we construct mathematical models of the target system. Traditionally, this has been based on a first-principles approach, using detailed knowledge of the target system. Such models can be complex and difficult to build, validate, use, and maintain. In our methodology, a statistical (ARMA) model is fit to historical measurements of the target being controlled. These models are easier to obtain and use and allow us to apply control-theoretic design techniques to a larger class of systems. When applied to a Lotus Notes groupware server, we obtain model fits with R/sup 2/ no lower than 75% and as high as 98%. In controller design, an analysis of the models leads to a controller that will achieve the service level objectives. We report on an analysis of a closed-loop system using an integral control law with Lotus Notes as the target. The objective is to maintain a reference queue length. Using root-locus analysis from control theory, we are able to predict the occurrence (or absence) of controller-induced oscillations in the system's response. Such oscillations are undesirable since they increase variability, thereby resulting in a failure to meet the service level objective. We implement this controller for a real Lotus Notes system, and observe a remarkable correspondence between the behavior of the real system and the predictions of the analysis. This indicates that the control theoretic analysis is sufficient to select controller parameters that meet the desired goals, and the need for simulations is reduced.

Logic controllers for machining systems typically have three control modes: auto, hand and manual. In this paper, a unified formal representation of logic controllers with three control modes is provided using Petri nets (PNs). A modular logic controller structure is introduced and formalized for high-volume transfer lines. The modular logic controller consists of one control module for the mode decision and other control modules for station logic controllers. Each station control module is represented by connecting together operation modules, which are designed with respect to the fault recovery processes of operations; their connection algorithm is also provided. In our formal representation, each control module is represented by a live, safe and reversible PN. A condition for the modular logic controller to generate a correct control logic is provided: the operation causality condition. Using the modular structure of a logic controller, the control logic can be easily reconfigured and automatic code generation is possible.

The logic controller for a machining system is a discrete event supervisory system. In high volume transfer lines, it consists of three control modes: auto, hand, and manual. A logic controller can achieve the goal of a machining system by its control logic. The paper addresses a formal Petri net representation of the control logic and its implementation. The control logic is represented by two Petri net models: one for the mode decision control logic and the other for the sequence control logic. Because the Petri net models are live, safe, and reversible, the actual logic control program can be generated directly from the Petri net models by using IEC1131-3 programming languages.

The machining systems considered are high volume transfer lines. The cycle time during normal operation is used as the performance metric. Normal operation is governed by the auto-cycle function of a logic controller. A modular logic controller is introduced and formalized for event-based functional properties analysis of transfer line. By adding time specifications, a timed modular logic controller is generated. The time-based cyclic behavior of high volume transfer lines is characterized using timed Petri nets. Two efficient algorithms to compute the cycle time and critical operations are developed. A method for improving the performance of a transfer line is introduced.

The machining systems considered are high volume transfer lines which are widely used in automotive manufacturing. In these machining systems, several machines linked together provide complete processing of a part. A logic controller is a discrete event supervisory system which controls parallel and synchronized sequences of elementary operations of each machine to achieve the goal of the machining system. Normal operation is governed by the auto-cycle function of a logic controller. A modular logic controller is introduced and formalized for high volume transfer lines. Its event-based functional properties are verified and its reconfigurability is considered. A live and safe marked graph can be directly transformed into a sequential function chart (which is one of the IEC 1131-3 languages) and, using this SFC representation, a modular logic controller can be implemented. The performance analysis of the modular logic controller is also introduced. The cycle time during normal operation is used as the performance metric of a transfer line. By adding time specifications to the model, a timed modular logic controller is generated. The time-based cyclic behavior of high volume transfer lines is thus characterized using timed Petri nets. Two efficient algorithms to compute the cycle time and critical operations are developed.

This paper examines the kinematic model of an autonomous mobile robot system consisting of a chain of steerable cars and passive trailers, linked together with rigid bars. The state space and kinematic equations of the system are defined, and it is shown how these kinematic equations may be converted into a multiinput chained form. The advantages of the chained form are that many methods are available for the open-loop steering of such systems as well as for point-stabilization; some of these methods are discussed here. Dynamic state feedback is used to convert the system to this multiinput chained form. It is shown how the dynamic state feedback that is used in this paper corresponds to adding, in front of the steerable cars, a chain of virtual axles which diverges from the original chain of trailers. Two different example systems are also presented, along with simulation results for a parallel-parking maneuver.

Methods for steering car-like robots with trailers are investigated. A connection is demonstrated between Murray and Sastry's (1990, 1991) work of steering with integrally related sinusoids and Sussmann and Liu's (1991) recent work on asymptotic behavior of systems with high-frequency sinusoids as inputs. The merits of coordinate transformations, relative to the convergence properties, are discussed. Simulation results for a car-like robot with two trailers are presented.>

A technique for stabilizing nonholonomic systems to trajectories is presented. It is well known that such systems cannot be stabilized to a point using smooth static-state feedback. The authors suggest the use of control laws for stabilizing a system about a trajectory, instead of a point. Given a nonlinear system and a desired nominal feasible trajectory, an explicit control law which will locally exponentially stabilize the system to the desired trajectory is given. The theory is applied to several examples, including a car-like robot.>