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.

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.

Owing to the multi-factorial nature of fatigue, leveraging context to effectively monitor and intervene with fatigue symptoms presents a significant challenge. This paper aimed to understand how to incorporate context into designing mHealth systems for fatigue management. We conducted a two-week field study with 20 fatigue-vulnerable individuals using an activity-tracking sensor and self-reporting. We conducted data-prompted interviews to explore phenomena about participants’ fatigue experiences. Findings show a heterogeneous relationship between context and fatigue, which can be attributed to the phenomena that: (1) participants were influenced by multiple fatigue-inducing factors for different durations; (2) broad contexts moderated participants’ perceptions and coping strategies in response to local contexts; (3) the predictability and repetition of activities influenced participants’ fatigue perception and coping strategies. We propose a context-informed layer-based framework integrating these phenomena and discuss implications for designing fatigue management tools informed by our framework.

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.

In this article, we develop a modular neural network for real-time (>10 Hz) semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural networks. Although robotic perception is often divided between modern differentiable methods and classical explicit methods, a union of both is necessary for real-time and trustworthy performance. We introduce a novel convolutional Bayesian kernel inference (ConvBKI) layer which incorporates semantic segmentation predictions online into a 3-D map through a depthwise convolution layer by leveraging conjugate priors. We compare ConvBKI against state-of-the-art deep learning approaches and probabilistic algorithms for mapping to evaluate reliability and performance. We also create a robot operating system package of ConvBKI and test it on real-world perceptually challenging off-road driving data.

In previous studies, researchers have successfully measured walking in healthy able-bodied humans to create safe control strategies for lower body assistive devices. measurements used to establish design requirements often come from testing and evaluation that takes place in laboratory settings during steady-state tasks, where participants often select movement strategies that minimize the cost of transport. However, human walking in these conditions does not neces-sarily represent the natural behavior of an individual in the real world. In this work, we conducted a study to characterize human walking in the real world. We combined week-scale free-living measurements of gait with in-lab data collection to: 1) quantify the proportion of steady-state walking in a population of healthy able-bodied adults, and 2) evaluate whether this population favors the selection of a range of walking speeds that minimize their cost of transport in the real world. We found that the majority of walking bouts contain mostly transient walking, suggesting that researchers should complement steady-state characterization with non-steady-state tasks. We also found that the most often used steady-state walking speeds for all participants were higher than the range that minimizes cost of transport, suggesting that individuals are influenced by more than energy economy when moving in the real world. Thus, when developing control strategies for these devices, researchers should consider a variety of optimization objectives to adapt for the multifarious situations of daily life.

Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (Con-vBKI) layer which learns to perform explicit Bayesian inference within a depthwise separable convolution layer to maximize efficency while maintaining reliability simultaneously. We apply our layer to the task of real-time 3D semantic mapping, where we learn semantic-geometric probability distributions for LiDAR sensor information and incorporate semantic predictions into a global map. We evaluate our network against state-of-the-art semantic mapping algorithms on the KITTI data set, demonstrating improved latency with comparable semantic label inference results.

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.

This article develops a stochastic programming framework for multiagent systems, where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed subtasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Owing to their inherent flexibility and robustness, multiagent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multiagent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, is scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.

For robot swarms operating on complex missions in an uncertain environment, it is important that the decision-making algorithm considers both heterogeneity and uncertainty. This paper presents a stochastic programming framework for the vehicle routing problem with stochastic travel energy costs and heterogeneous vehicles and tasks. We represent the heterogeneity as linear constraints, estimate the uncertain energy cost through Gaussian process regression, formulate this stochasticity as chance constraints or stochastic recourse costs, and then solve the stochastic programs using branch and cut algorithms to minimize the expected energy cost. The performance and practicality are demonstrated through extensive computational experiments and a practical test case.

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.

Tracking of uncontrollable agents in a controlled environment is an important research question for the coordination of controllable and uncontrollable agents and bio-inspired multi-agent control. This paper presents a framework that approaches the multiagent tracking problem from a localization perspective, utilizing a combination of wearable sensors and stationary cameras. Specifically, this framework was applied to localize uncontrollable biological agents (dolphins) in a well defined environment. The biological agents were outfitted with wearable sensors (IMU, speed, depth) and were free to move in their three dimensional habitat. The dynamic data collected by the wearable sensors was supplemented with image data collected using a pair of cameras mounted above the habitat. The framework presented in this paper combines data from these sensor streams to calculate an accurate estimate of the animal's location during extended periods of free movement. The associations between camera detections and tagged agents are handled using a particle filter embedded with a fuzzy observation concept. The platform is readily implementable in similar water / land environments, and is able to handle nonlinear agent dynamics, non-Gaussian noise, and sparse camera observations while maintaining robust agent localization and tracking.

Safety violations in programmable logic controllers (PLCs), caused either by faults or attacks, have recently garnered significant attention. However, prior efforts at PLC code vetting suffer from many drawbacks. Static analyses and verification cause significant false positives and cannot reveal specific runtime contexts. Dynamic analyses and symbolic execution, on the other hand, fail due to their inability to handle real-world PLC programs that are event-driven and timing sensitive. In this paper, we propose VetPLC, a temporal context-aware, program analysis-based approach to produce timed event sequences that can be used for automatic safety vetting. To this end, we (a) perform static program analysis to create timed event causality graphs in order to understand causal relations among events in PLC code and (b) mine temporal invariants from data traces collected in Industrial Control System (ICS) testbeds to quantitatively gauge temporal dependencies that are constrained by machine operations. Our VetPLC prototype has been implemented in 15K lines of code. We evaluate it on 10 real-world scenarios from two different ICS settings. Our experiments show that VetPLC outperforms state-of-the-art techniques and can generate event sequences that can be used to automatically detect hidden safety violations.

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.

Accurate biological agent localization offers the opportunity for both researchers and institutions to gain new knowledge about individual and group behaviors of biosystems. This paper presents a sensor-fusion approach for tracking biological agents, combining the data from automated video logging with magnetic, angular rate, and gravity (MARG) and inertial measurement unit (IMU) data, with professionally managed dolphins as the representative example. Our method of video logging allows for accurate and automated dolphin location detection using a combination of Laplacian of Gaussian (LoG) and multi-orientation elliptical blob detection. These data are combined with MARG/IMU measurements to generate a localization estimate through a series of drift-correcting Kalman and gradient-descent filters, finalized with Incremental Smoothing and Mapping (iSAM2) pose-graph localization.