Search

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The objective of this paper is to facilitate the improvement of the control and operation of industrial facilities, by providing decision support tools. More specifically, this paper has three main contributions. First, this paper presents the definition of operation metrics that provide insight into the behavior of: 1) annunciated alarms, 2) alarm floods, and 3) operator actions in industrial facilities. Second, this paper presents visualization plots named multilayered radar plots that can present information in an elegant, dense, and comprehensive fashion. Three types of plots are proposed, which collectively compare the behavior of metrics, variables, and operation times in industrial facilities. Third, this paper presents a ranking method and a reordering design procedure of displayed alarms during an alarm flood to reorder the alarms based on the proposed alarm-flood criticality index. The purpose is to provide additional assistance to operators to focus on more critical issues. As the operation metrics, visualization plots, and the ranking in alarm floods heavily utilize historized data and given the industrial-oriented application of these decision support tools, this paper also addresses the extraction of information and the integration of the tools into industrial automation platforms. Note to Practitioners—In a control system used for an industrial facility, a large amount of data is collected and historized. The data include sensor measurements, status of actuators, alarms, and operator actions, and it therefore contains valuable information. The information can be extracted and utilized to assist in the improvement of the control and operation of the industrial facility. The objective of this paper is to provide decision support tools by: 1) defining operation metrics that can characterize the information extracted from the historized data; 2) presenting visualization plots that allow for a clear presentation and comparison of the metrics, where three types of plots are proposed for different purposes of comparison; and 3) a ranking method and a reordering design procedure of displayed alarms during an alarm flood. Furthermore, this paper discusses how the information is extracted from the historized data and how to integrate the proposed decision support tools into existing industrial automation platforms. [ABSTRACT FROM AUTHOR]

Threaded fasteners are prevalent throughout modern manufacturing. Thus, as the demand for automation in manufacturing increases, so does the demand for automated threaded fastening systems. However, many fundamental issues and engineering challenges still hinder robustness in automation, particularly for smaller screws and critical product finishing requirements. This paper surveys the state of the art in threaded fastening automation and discusses open questions for further research. This survey covers the following areas: 1) fundamentals of threaded fastening, including basic concepts and definitions; 2) analysis of the entire assembly process (consisting of part feeding and orientation, pickup, alignment, and driving), including discussions of tools, control strategies, and other considerations; 3) failure modes and techniques to mitigate them; 4) threaded fastening systems and electromechanical approaches; and 5) open challenges and suggestions for future development. Understanding the current state of automation in threaded fastening will provide a foundation for researchers to advance this field. Note to Practitioners—This paper is motivated by the problem of an automated assembly of small screws, one of the most challenging problems in a smartphone assembly. It represents a rigorous review of robotic screwdriving literature to identify the state-of-the-art and open problems. The review material was targeted toward engineers working on related problems with the sponsor and has proved useful to them. To benefit researchers in the field of robotic and automated assembly, we have compiled the review material in the form of a survey paper. This paper covers theoretical fundamentals, tools, control and failure detection strategies, industrial applications, and open problems for robotic screwdriving. It provides a foundation for readers to familiarize themselves with the state of the art and conduct further research on this thread. [ABSTRACT FROM AUTHOR]

In this paper, an automatic vision-guided micromanipulation approach to facilitate versatile deployment and portable setup is proposed. This paper is motivated by the importance of micromanipulation and the limitations in existing automation technology in micromanipulation. Despite significant advancements in micromanipulation techniques, there remain bottlenecks in integrating and adopting automation for this application. An underlying reason for the gaps is the difficulty in deploying and setting up such systems. To address this, we identified two important design requirements, namely, portability and versatility of the micromanipulation platform. A self-contained vision-guided approach requiring no complicated preparation or setup is proposed. This is achieved through an uncalibrated self-initializing workflow algorithm also capable of assisted targeting. The feasibility of the solution is demonstrated on a low-cost portable microscope camera and compact actuated microstages. Results suggest subpixel accuracy in localizing the tool tip during initialization steps. The self-focus mechanism could recover intentional blurring of the tip by autonomously manipulating it 95.3% closer to the focal plane. The average error in visual servo is less than a pixel with our depth compensation mechanism showing better maintaining of similarity score in tracking. Cell detection rate in a 1637-frame video stream is 97.7% with subpixels localization uncertainty. Our work addresses the gaps in existing automation technology in the application of robotic vision-guided micromanipulation and potentially contributes to the way cell manipulation is performed. Note to Practitioners—This paper introduces an automatic method for micromanipulation using visual information from microscopy. We design an automatic workflow, which consists of: 1) self-initialization; 2) vision-guided manipulation; and 3) assisted targeting, and demonstrate versatile deployment of the micromanipulator on a portable microscope camera setup. Unlike existing systems, our proposed method does not require any tedious calibration or expensive setup making it mobile and low cost. This overcomes the constraints of traditional practices that confine automated cell manipulation to a laboratory setting. By extending the application beyond the laboratory environment, automated micromanipulation technology can be made more ubiquitous and expands readily to facilitate field study. [ABSTRACT FROM AUTHOR]

The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this paper, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which uses a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning, we use global state-of-the-art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed. Note to Practitioners—This paper was motivated by the current deficit to automate maintenance tasks in manufacturing systems. Today there exist many automatic methods for fault detection, but there is no possibility to restore the desired state. A typical application is the replacement of a defective component or service tasks such as refilling of cooling lubricant in machine tools. The focus of this paper is to provide automatic planning concepts for robot systems which deal with this problem. For this, a task planning algorithm is proposed which uses CAD and vision data. The advantage of this method is that it works on a contact analysis, based on a discrete approximation of the disassembly space, allowing a fast determination of possible robot manipulations. Also, a new preprocessing concept for path planning is developed which enables the adaptation of the stepsize of the path planner, based on the local occupancy of the environment. Through this, it is possible to drastically reduce the required planning effort. [ABSTRACT FROM AUTHOR]

Compositional Optimization (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a modular learning that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization. Note to Practitioners—Automation systems are becoming increasingly large and complex and the automation of more and more advanced tasks often requires the coordination of multiple subsystems. Optimization can have a great impact on the efficiency of these systems in terms of cost and operation speed. Finding optimal solutions is, however, a difficult task. As the number of tasks and subsystems of the automation system increases, the search space of the optimization problems tends to grow exponentially. Compositional Optimization (CompOpt) is a method specifically designed for the optimization of large-scale automation systems. A challenge with the application of CompOpt is that it takes as input a specific type of optimization model that divides the system into subsystems; like machines, vehicles, etc. Formulating these models requires a high level of expertise and system knowledge. This paper addresses this challenge with an algorithm that learns these models from a simulation of the system. The simulation can be implemented in any software as long as a suitable interface exists or can be constructed. To divide the learning into subsystems, the algorithm uses an initial plant structure hypothesis (PSH). This can be viewed as a meta-model that includes known structural information, such as the number of subsystems and which actions that affect each subsystem. The more structural information that is added to PSH, the more efficient the learning and subsequent optimization will be. The purpose of this is to reduce the level of expertise and system knowledge needed in the application of optimization, to simplify the transition to Industry 4.0. [ABSTRACT FROM AUTHOR]

Polyculture farming, where multiple crop species are grown simultaneously, has potential to reduce pesticide and water usage while improving the utilization of soil nutrients. However, it is much harder to automate polyculture than monoculture. To facilitate research, we present AlphaGardenSim, a fast, first order, open-access polyculture farming simulator with single plant growth and irrigation models tuned using real world measurements. AlphaGardenSim can be used for policy learning as it simulates inter-plant dynamics, including light and water competition between plants in close proximity and approximates growth in a real greenhouse garden at 25, $000\times $ the speed of natural growth. This paper extends earlier work with a new action space that includes planting, which dynamically finds new seed locations that increases resources utilization, and an adaptive sampling technique to reduce the number of actions taken at each timestep without affecting performance. We also evaluate other automation policies using a novel metric that combines plant diversity and canopy coverage. Code and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden. Note to Practitioners—Monoculture farming is often characterized by heavy agrichemical inputs, such as chemical fertilizers and pesticides, and increased vulnerability to disease and pestilence. This paper is motivated by the lack of long-term sustainability of industrial agriculture, and its implications for human food security. Although polyculture is a sustainable alternative to monoculture farming, it requires more human labor and is more challenging to automate. In this paper we propose a fast, first order simulator that simulates the growth of plants in a polyculture setting. Simulation experiments suggest that the simulator can be used to learn a planting, watering and pruning plan a robot can follow to produce maximal yield from a diverse set of plants with limited irrigation, however it has not yet been tested on a physical garden. In future research we will develop a fully automated controller that will operate planting, irrigation and pruning tools in a physical garden over multiple plant growth cycles. [ABSTRACT FROM AUTHOR]

As innovative energy-related technologies are rapidly developing, new elements are populating the electrical power grid, such as unpredictable renewable energies, plug-in electric vehicles (EVs), smart electrical home appliances, energy efficient homes/buildings, and distributed energy storage. All these elements induce a paradigm shift in power grids, which are evolving from producer-controlled structures to large, distributed, and customer-interactive ones, enhancing the coupling between the physical and the information layer. In order to be effectively employed on a large scale, all these elements must rely on automation, as well as on optimization methods, for the efficient management and consumption of energy resources under varying stochastic conditions. Driven by these changes, power grids have become complex and large-scale platforms with growing needs for automation and optimal energy management services, a concept that can be referred to as smart grids. [ABSTRACT FROM AUTHOR]

This paper presents an automated robotic system for generating semantic maps of inventory in retail environments. In retail settings, semantic maps are labeled maps of stores where each discrete section of shelving is assigned a department label describing the types of products on that shelf. Starting from a metric map of the store, the robot autonomously extracts the shelf boundaries, generates a distance-optimal tour of the store to view every shelf, and follows the tour while avoiding unmapped clutter and moving people. The robot creates a point cloud of the store using the data collected from this tour. We introduce a novel soft-object assignment algorithm to create a virtual map and a dynamic programming algorithm to segment this map. These algorithms use a priori information about the products to boost data from laser and camera sensors in order to recognize and semantically label objects. The primary contribution of this paper is the integration of multiple systems for automated path planning, navigation, object recognition, and semantic mapping. This paper represents an important contribution toward deploying mobile robots in dynamic human environments. [ABSTRACT FROM AUTHOR]

This paper presents a controlled tying approach for knots using fixtures and simple pulling motions applied to the ends of string. Each fixture is specific to a particular knot; the paper gives a design process that allows a suitable fixture to be designed for an input knot. Knot tying is separated into two phases. In the first phase, a fixture is used to loosely arrange the string around a set of rods, with the required topology of the given knot. In the second phase, the string is pulled taut around the tightening fixtures. Two tightening fixture designs are presented. The first design is a fixture with no moving parts; tilted rods whose cross-sections get closer near the tips, guiding string in a controlled fashion as string slides up the rods during tightening. The second design is a collection of straight rods that can move passively along predefined paths during tightening. Successful tying is shown for three interesting cases: a “cloverleaf knot” design, a “double coin” knot design, and the top of a shoelace knot. [ABSTRACT FROM AUTHOR]

This special section of the IEEE Transactions on Automation Science and Engineering (T-ASE) focuses on new models, methods, and technologies for energy efficiency and sustainability in transportation and energy systems. In this section, the focus is thus on articles considering sustainable transportation, such as electric vehicles (EVs), integrated with the smart grid requirements. As guest editors, we are very pleased to present the selected 12 papers, whose topics are specifically related to optimal planning of charging stations (CSs), sustainable transportation and mobility, EVs integration in smart grids, reliability, reduction of consumption, demand response and smart grid modeling, optimal scheduling, routing and charging of fleets of EVs, as well as smart parking. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Programming by demonstration (PBD) transforms the robot programming from the code level to automated interface between robot and human, promoting the flexibility of robotized automation. In this paper, we focus on programming the industrial robot for assembly tasks by parsing the human demonstration into a series of assembly skills and compiling the skill to the robot executables. To achieve this goal, an identification system using multimodal information to recognize the assembly skill, called MASD, is proposed including: 1) an initial learning stage using a hierarchical model to recognize the action by considering the features from action-object effect, gesture, and trajectory and 2) a retrospective thinking stage using a segmentation method to cut the continuous demonstrations into multiple assembly skills optimally. Using MASD, the demonstration of assembly tasks can be explained with high accuracy in real time, driving a hypothesis that a PBD system on the top of MASD can be extended to more realistic assembly tasks beyond pure positional moving and picking. In experiments, the skill identification module is used to recognize the five kinds of assembly skills in demonstrations of both single and multiple assembly skills, and outperforms the comparative action identification methods. Besides integrated with the MASD, the PBD system can generate the program based on the demonstration and successfully enable an ABB industrial robotic arm simulator to assemble a flashlight and a switch, verifying the initial hypothesis. Note to Practitioners—In the conventional robotized automation, the key role of the robot mainly owes to its capacity for repeating a wide variety of tasks with high speed and accuracy in long term, with a cost of days to months of programming for deployment. On the other hand, the new trend of customization brings the new characteristics: production in short cycle and small volume. This irreversible momentum urges the robot to switch from task to task efficiently. The biggest bottleneck here is the tedious programming, which also has high prerequisites for most practitioners in manufacturing. This situation motivates the development of a PBD system that can understand the assembly skills performed by the human experts in the demonstration and accordingly generate the program for robot’s execution of the taught task. In this paper, we present a skill decoding system to parse the observational raw demonstration into symbolic sequences, which is the crucial bridge to enable the automatic programming. The system achieves high performance in recognition and is tailored for the PBD in assembly tasks by considering both advantages and disadvantages in the background of assembly, such as controllable environment and limited computational resources. It is particularly useful for assembly tasks with modularized actions based on a set of standard parts. At the perspective of industrial application, the PBD upon the proposed system is a promising solution to improve the flexibility of manufacture, which is expected to be true in midterm but an important step toward this goal. [ABSTRACT FROM AUTHOR]

The central theme of this Special Issue is emerging opportunities and future directions in automation for green manufacturing, where information technology basedmodeling, analysis, control and optimization are the focus areas. The purpose is to show the state-of-the-art research and applications in the general area of automation in green manufacturing, by bringing together researchers and practitioners from both academia and industry, to address the significant advancement, expose the unsolved challenges, present the critical needs for integration with new technologies, and provide visions for future research and development. This Special Issue presents original, significant and visionary automation papers describing scientific models, methods and technologies with both solid theoretical development and practical importance that improve process, efficiency, productivity, quality, and reliability in green manufacturing. The contributions in this Special Issue can be divided into the following categories in green manufacturing: renewable energy products and systems; energy savings in manufacturing processes and systems; remanufacturing system design; and emission reduction in supply chain management. Specifically, the following papers are included in this Special Issue. The first category addresses renewable energy source, which includes manufacturing and operation of alternative energy products, such as batteries for electric vehicles, and system design to support manufacturing activities using renewable energy sources, such as electricity generated by wind turbines and solar panels. The second category focuses on energy savings in manufacturing, such as operational control and robot scheduling to minimize energy consumptions in production, optimal design of facility and production to reduce energy cost. The third category extends the study to remanufacturing, which plays a significant role to achieve sustainability and multiple life cycles. This includes remanufacturing system analysis, design and optimization, such as strategies for recycling, reassembly, etc. Finally, the last category considers supply chain management, within which multiple firms work together to reduce negative environmental impact. [ABSTRACT FROM AUTHOR]

The advance of new sensing technologies, such as the 3-D laser scanning, creates a data-rich environment for quality control in modern industries. The free-form surfaces of complex manufactured parts can be quickly scanned, producing thousands of data points. To monitor these large-scale surface scanning data, three major challenges have to be solved simultaneously: 1) simple parametric models are no longer sufficient to describe free-form surfaces; 2) the massive data points need fast computations; and 3) the presence of outliers calls for robust analytics. To fulfill this task, this paper proposes a novel monitoring scheme where the control chart is designed based on a new robust bilateral kernel smoothing method. A fast approximation algorithm is also developed for efficient online monitoring. This fast and robust nonparametric control chart shows significant superiority for surface monitoring in our numerical simulations. Finally, a real case study demonstrates the effectiveness of our proposed scheme in monitoring the stability of a 3-D printing process. Note to Practitioners—Recently, the widespread use of 3-D laser scanners in industries enables engineers to digitize any free-form surface into thousands of data points, which provides an unprecedented opportunity for quality control of complex manufactured products. This paper proposes a monitoring scheme which caters to arbitrary geometric features on a surface. Practitioners need no prior and specific surface mathematical models. A robust estimate of the surface can be quickly derived from the noisy scanning data by an approximation algorithm. Then, a statistic calculating the deviation of this surface from the target one is plotted in a control chart to monitor the stability of the manufacturing process. In practice, this monitoring scheme can be integrated with the 3-D laser scanner as a new production module for automatic quality control and decision making. [ABSTRACT FROM AUTHOR]

Many operation optimization problems such as scheduling and assignment of interest to the automation community are mixed-integer linear programming (MILP) problems. Because of their combinatorial nature, the effort required to obtain optimal solutions increases drastically as the problem size increases. Such operation optimization problems typically need to be solved several times a day and require short solving times (e.g., 5, 10, or 20 min). The goal is, therefore, to obtain near-optimal solutions with quantifiable quality in a computationally efficient manner. Existing MILP methods, however, suffer from slow convergence and may not efficiently achieve this goal. In this paper, motivated by fast convergence of augmented Lagrangian relaxation (LR), a novel advanced price-based decomposition and coordination “surrogate absolute-value LR” (SAVLR) approach is developed. Within the method, convergence of our recent surrogate LR (SLR), which has overcome all major difficulties of traditional LR, is significantly improved by penalizing constraint violations by adding “absolute-value” penalties. Moreover, such penalties are efficiently linearized in a standard way, thereby enabling the use of MILP solvers. By exploiting the beautiful property of exponential reduction of complexity of subproblems upon decomposition, subproblems are efficiently solved and their solutions are efficiently coordinated by updating Lagrangian multipliers. Convergence is then proved under novel adjustment of penalty coefficients. A series of generalized assignment problems is considered, and for these problems, superior performance of SAVLR over other state-of-the-art and state-of-the-practice methods is demonstrated. Accompanying CPLEX codes, whereby SAVLR is implemented, are also included. Note to Practitioners—Examples of important problems that arise in automation community include scheduling and assignment problems. Because of their combinatorial nature, the effort required to obtain optimal solutions increases drastically as the problem size increases. Existing mixed-integer linear programming (MILP) methods, however, may suffer from slow convergence and may not efficiently achieve this goal. The new method revolutionizes the way such problems can be solved with major improvements on the overall performance. It is based on our recent breakthrough “surrogate Lagrangian relaxation” (LR), which has overcome all major difficulties of traditional LR while exploiting the beautiful property of exponential reduction of complexity upon decomposition. To significantly improve convergence while maintaining linearity so as to use MILP solvers, our idea is to penalize violations of relaxed constraints by the infrequently used “absolute-value” penalty functions. Although not differentiable, absolute-value penalties have the advantage of being exactly linearizable through extra variables and constraints. The difficulties caused by those extra constraints, which couple subproblems, are resolved by adaptive adjustment of penalty coefficients. A series of generalized assignment problems is considered and superior performance of the new method against state-of-the-art and state-of-the-practice methods is demonstrated. Accompanying CPLEX codes whereby the new method is implemented are also included. [ABSTRACT FROM AUTHOR]

Circuit widths and nodes of semiconductor wafers have been continually shrinking down to less than 20 nm. Therefore, modern wafer fabs enforce extremely strict process control to prevent wafer quality failures. A wafer processing chamber is now frequently cleaned to remove residual chemicals and impurities. Yu et al. show that such cleaning operations significantly change the tool operation of single-armed cluster tools, and they suggest an idea of partial wafer loading to improve the tool throughput under cleaning requirements. However, little is known about how a dual-armed tool could be effectively scheduled when chamber cleaning exists. A dual-armed robot allows more flexible tool operational sequences, and hence, the scheduling problem becomes further complicated and challenging. In this paper, we propose a scheduling method by which the dual arms can be properly exploited for better tool productivity. We show that the suggested hybrid sequence significantly reduces the tool cycle time as compared to previously developed scheduling methods. Through this research, we conclude that the productivity gain of the dual arms against single arm is more significant when chambers are cleaned. Note to Practitioners—This paper is motivated by the scheduling issues of cluster tools with chamber cleaning requirements. Though an efficient scheduling method for single-armed cluster tools is presented by Yu et al., its performance is not sufficiently close to the optimal when a dual-armed robot is considered. More specifically, we cannot expect any productivity improvement from adopting a dual-armed robot, although it allows more flexible tool operations and is known to have higher throughput than a single-armed robot. Thus, we propose a novel scheduling method for dual-armed cluster tools that require chamber cleaning operations. The experimental results show that the suggested method achieves optimal tool throughput in most of the practical tool conditions. [ABSTRACT FROM AUTHOR]

In this paper, a machine learning (ML) approach is presented that exploits accelerometers data to deal with gesture recognition (GR) problems. The proposed methodology aims at providing high accuracy classification for home automation systems, which are generally user independent, device independent, and device orientation independent, an heterogeneous scenario that has not been fully investigated in previous GR literature. The approach illustrated in this paper is composed of three main steps: event identification; feature extraction; and ML-based classification. The elements of the novelty of the proposed approach are 1) a preprocessing phase based on principal component analysis to increase the performance in real-world scenario conditions and 2) the development of parsimonious novel classification techniques based on sparse Bayesian learning. This methodology is tested on two datasets of four gesture classes (horizontal, vertical, circles, and eight-shaped movements) and on a further dataset with eight classes. In order to authentically describe a real-world home automation environment, the gesture movements are collected from more than 30 people who freely perform any gesture. It results in a dictionary of 12 and 20 different movements, respectively, in the case of the four-class and the eight-class databases. [ABSTRACT FROM AUTHOR]

Soft robots must be able to structure an automation problem into a sequence of actions that lead to a desired state, before they can fulfill a meaningful role in automation applications. This, however, can only be successful if the robot can predict the outcome of an action. The theory of rigid industrial robots is not applicable without major changes, because kinematic chains do not adequately describe the continuous deformation of the complex, often biologically inspired shapes of soft robots. Analytic solutions have not been found yet. Numerical solutions based on finite elements are slow, technically challenging, and only suitable for one specific robot. It is, however, possible to observe the outcome of an action, and use these observations to plan a sequence of actions that let the robot accomplish an automation task. In this paper, we analyze a probabilistic automaton that computes the optimal sequence of actions to bring the robot into a desired state. An earlier article explained the functioning of the method in a toy example. In this paper, we analyze if it is feasible to apply the method to a planning problem inspired by a real soft robot. We show the results and document the planning process. We identify the analog of an impulse response, although it is not closed form due to the nonparametric nature of the method.

Note to Practitioners—A soft robotic sorting table has a computer-controlled soft surface that can move delicate objects without damaging them. There are currently no closed-loop control systems for such robots, because it is unclear how to relate the control signals to the behavior of the table, or which actions to choose in order to solve a manipulation task. In this paper, we propose a probabilistic automaton to plan the best action sequence on average. The sequence brings the workpieces on top of a soft table into a desired condition. It is a machine learning solution that is based on observations of the input signals and their effect, rather than a detailed analytical or numerical modeling of the robot. We show that it is feasible to model an existing soft robotic table. We demonstrate that the planning is successful by solving complex maze tasks. Our results are based on experiments and simulations. [ABSTRACT FROM AUTHOR]

The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual–tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual–tactile data samples. Finally, we develop a visual–tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

This paper proposes a Petri net (PN) representation of the activities performed by the key human operators for unloading/loading containers in an intermodal maritime container terminal (CT) with a low level of automation. These processes are the core of the export, import, and transshipment cycles executed in the terminal. The aim of this paper is to consider both the human component and the material handling resources, e.g., cranes and transporters, by defining an accurate model, which describes how to coordinate humans and use the system resources necessary for serving mother or feeder ships. The developed generalized stochastic PN-based model is of limited complexity and represents a complete, unambiguous, and readable model of the target process before coding it in the target simulation tool. The modular integrated model is tested and validated by the simulation of typical and perturbed scenarios of the Taranto CT, a real terminal that is taken as a case study for its complexity and similarity to CTs with multiple transport modes. [ABSTRACT FROM PUBLISHER]

In this paper, a method is presented for lowering the energy consumption and/or increasing the speed of a standard manipulator spray painting a surface. The approach is based on the observation that a small angle between the spray direction and the surface normal does not affect the quality of the paint job. Recent results in set-based kinematic control are utilized to develop a switched control system, where this angle is defined as a set-based task with a maximum allowed limit. Four different set-based methods are implemented and tested on a UR5 manipulator from Universal Robots. Experimental results verify the correctness of the method, and demonstrate that the set-based approaches can substantially lower the paint time and energy consumption compared to the current standard solution. Note to Practitioners—This paper is motivated by the observation that uniform paint coating can be achieved even when the spray direction is not normal to the spray surface. By allowing a small orientation error, the manipulator maintains a constant spray velocity along the surface while following a more energy efficient trajectory at the end-effector. By doing so, this velocity can also be increased as the proposed method requires less torque during the turns than the current industry standard. The method proposed in this paper is based on a kinematic control method and generates reference trajectories for the manipulator configuration and velocity every time step which are inputs to the manipulator dynamic controller. If tracked, the references will allow the paint gun to follow the trajectory, including both straight line segments and turns, with constant velocity and thus achieve uniform paint coating. We show how to calculate the reference state of the manipulator and present the explicit expressions for solving and implementing the algorithms. The approach can also be used for other applications where an offset in the end-effector orientation improves performance, such as welding and 3-D printing. [ABSTRACT FROM AUTHOR]

Controlling the gripping force on fragile objects has been a challenging task for industrial grippers. The solution often requires an electric gripper with embedded force sensors and control feedback. This approach is costly and leads to extra gripper complexity. To avoid damage while handling fragile objects, this paper presents a novel force regulation mechanism (FRM) to be installed on pneumatic grippers. Without using additional sensors and control, the FRM can passively produce an adjustable contact force between the gripper jaws and objects of various sizes. Together with pneumatic grippers that have a higher gripping speed, lower cost, and simpler structure, this approach offers a more attractive solution than the use of electric grippers. In this paper, the design and analysis of the FRM are presented. A prototype of the FRM is illustrated to demonstrate the effectiveness and accuracy of force regulation. This novel mechanism is expected to serve as a reliable alternative for fragile object manipulation. Note to Practitioners—Pneumatic grippers are popular due to their low cost and simple structure. However, it is difficult to control the gripping force to handle fragile objects without damage. The FRM has a small size and can be easily installed on pneumatic grippers to provide fast force regulation. The gripping force depends on axial preload of the FRM rather than air pressure of the gripper. Axial preload is passively regulated using a screw or spacer. No additional source of power or sensor is required. The FRM has a large force adjustment range and can grip objects of various sizes using the same force. Applications include the automation in electronics, food, agricultural, and medical industries. [ABSTRACT FROM AUTHOR]

Automated inspection systems play an important role in manufacturing to guarantee higher quality and reduce production costs. In the semiconductor manufacturing industry, assembly and testing processes are getting more complex, resulting in a greater tendency of defects to impact the production process. These defects can cause field failures and can result in customer dissatisfactions and returns. Currently available defect detection and classification systems are customized and hard-wired to the detection of particular classes of defects and cannot deal with new unknown classes of defects. This issue is aggravated by the very small sample size of available anomalies for learning, by the data imbalance problem, since the number of defective samples is significantly much smaller than the number of normal samples, and by the presence of noise. This paper presents a novel multifeature, sparse-based defect detection and classification approach that uses the stacking concept to enhance the classification accuracy. The stacking-based classifier is augmented with a novel adaptive over/downsampling technique to deal with the data imbalance problem. A new pruning technique is proposed to eliminate bad base learners. Shortage of defective units, similarities within different classes of defects, wide variation within the same defect class, and data imbalance are the basic challenges to deal with. Experimental results on real-world data from Intel show that the proposed approach results in a high classification accuracy as compared with the existing methods.

Note to Practitioners—The basic motivation of this paper is to design an automated cost-effective, adaptive, and intelligent defect detection and classification system that is easy to train using a small-size sample set of defects and that is robust to noise. The system is scalable in terms of the numbers and types of defects and features, which leads to a shorter development cycle. The presented system is immediately applicable to different types of defects. Inputs of the system are grayscale images. These images are processed to perform defects detection, features extraction, and classification. [ABSTRACT FROM PUBLISHER]

The ongoing coronavirus disease 2019 (COVID-19) is a pandemic causing millions of deaths, devastating social and economic disruptions. Testing individuals for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of COVID-19, is critical for mitigating and containing COVID-19. Many countries are implementing group testing strategies against COVID-19 to improve testing capacity and efficiency while saving required workloads and consumables. A group of individuals’ nasopharyngeal/oropharyngeal (NP/OP) swab samples is mixed to conduct one test. However, existing group testing methods neglect the fact that mixing samples usually leads to substantial dilution of viral ribonucleic acid (RNA) of SARS-CoV-2, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. To achieve this goal, we propose an Adaptive Group Testing (AdaGT) method. By collecting information on the number of positive and negative samples that have been identified during the screening process, the AdaGT method can estimate the ratio of positive samples in real-time. Based on this ratio, the AdaGT algorithm adjusts its testing strategy adaptively between an individual testing strategy and a group testing strategy. The group size of the group testing strategy is carefully selected to guarantee that the sensitivity of each test is higher than a predetermined threshold and that this group contains at most one positive sample on average. Theoretical performance analysis on the AdaGT algorithm is provided and then validated in experiments. Experimental results also show that the AdaGT algorithm outperforms existing methods in terms of efficiency and sensitivity. Note to Practitioners—Real-time reverse transcription-polymerase chain reaction (rRT-PCR) tests provide scope for automation and are one of the most widely used laboratory methods for detecting the SARS-CoV-2 virus. This paper is motivated by the following challenges: (1) Many countries are experiencing an acute shortage of professionals and consumables for conducting rRT-PCR tests; (2) Group sizes of existing group testing methods against COVID-19 may not be optimal, which adversely impacts the efficiency of the screening of the SARS-CoV-2 virus; (3) Existing group testing methods do not consider the fact that the sensitivity of rRT-PCR tests usually decreases with the group size. The objective of this paper is to improve the efficiency and sensitivity of large-scale screening against COVID-19. For achieving this goal, we propose an Adaptive Group Testing (AdaGT) algorithm, which has the following advantages: (1) It can improve the efficiency for screening the SARS-CoV-2 virus, mainly by adaptively adjusting its testing strategy between an individual testing strategy and a group testing strategy based upon an estimated ratio of positive samples during the screening process; (2) It can guarantee a high sensitivity of the rRT-PCR tests by determining the group sizes of the group testing strategy based upon some constraints; (3) We derive an appropriate threshold for the estimated ratio of positive samples such that the AdaGT algorithm can achieve a minimum average number of rRT-PCR tests and can be directly employed in practical applications. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

The IEEE Transactions on Automation Science and Engineering (T-ASE) wishes to thank the 987 reviewers over the past year who have performed an essential role in maintaining the quality of this publication. T-ASE strives for the 90–90 standard, i.e., 90% of papers should be reviewed within 90 days of submission. The review process starts when the Editor-in-Chief assigns the paper to an Editor, who assigns it to an Associate Editor (AE). The AE then obtains confirmation from 3–5 reviewers, carefully studies the results, and makes a recommendation to the Editor, who then makes a decision that is reported back to the lead author. This review process is very demanding, especially under the time constraints. We have summarized the process under “Guidelines for T-ASE Editors and AEs for Handling Papers” on our website. I am pleased to report that submissions have steadily increased (over 1000 in 2016) and our Impact Factor is now 3.502, an 16.5% increase over last year. As we strive to increase the quality and impact of the journal, we rely on the hard work of our reviewers and members of the Editorial Board. To those dedicated volunteers, and to anyone who may have been inadvertently omitted from the list below, we extend our sincere appreciation. [ABSTRACT FROM PUBLISHER]

During the virtual development and experimental testing of advanced construction machinery, automation approaches for automated task execution can prove very valuable. In this paper, modeling and automation approaches for a hydraulic mini excavator are developed. In particular, a physical model for detailed system analysis and a simplified Hammerstein model for controller tuning are developed and validated with measurement data from the mini excavator. For attitude estimation of the excavator, inertial measurement units and extended Kalman filters are used in a sensor fusion framework. The control concept for automation is based on a virtual driver consisting of a state machine for task coordination as well as offset-free model predictive controllers (MPCs) for decentralized and robust tracking control of all motion axes. The constrained MPC optimization problems are solved in real time by means of the accelerated proximal gradient method. Experimental results from the mini excavator prove the developed control approach to be valuable for virtual development and automated testing during the commissioning of hydraulic machinery.

Note to Practitioners—In this paper, a hydraulic mini excavator is considered for demonstrating the benefits of automation with regard to the development of advanced mobile machinery. A detailed physical model and a simplified model are introduced for virtual analysis and comissioning of the excavator. This allows for detailed system analysis even at an early development stage. Then, a framework for automated testing of the real prototype is introduced. This concept is based on attitude estimation filters, a state machine, and model predictive controllers and closely resembles the human driver in its behavior, but allows for reproducible testing results and therefore reduces commissioning efforts and development costs. Particular attention is paid to the robustness of the control concept, since the coupling of the hydraulic axes and digging forces lead to disturbances that need to be compensated. A simple, yet efficient and real-time capable algorithm is provided for numerical optimization. Experimental results show that the developed methods can contribute to the automation of hydraulic machinery, and that the introduced framework can easily be extended in order to automate other types of machinery with simple hydraulics. [ABSTRACT FROM AUTHOR]

A systematic method to evaluate the conflict between robustness, stability, and energy consumption is proposed in this paper. Energy optimization is combined with robust scheduling techniques to analyze the tradeoff. In rescheduling, slack is often used to protect a schedule from disruptions. However, results from the literature on energy minimization show that a reduction in energy consumption is achieved by extending the execution time of operations. Thus, slack in schedules is diminished on behalf of longer execution times. The proposed method, which quantitatively shows this conflict, is based on a multiobjective optimization formulation where efficient computation of the involved criteria is developed. This includes a convex surrogate stability measure that makes it possible to evaluate different operation sequences by a mixed-integer nonlinear programming formulation. Previous works connecting the two research fields use simulation for analyzing the impact of disruptions in order to generate robust production schedules. Our results show that an increase in energy efficiency comes at a cost of reducing stability and robustness and hence becoming more sensitive to disruptions. [ABSTRACT FROM AUTHOR]

In human–computer interactive systems (HCISs), there are not only autonomous robots completely controlled by computers but also semiautonomous robots requiring human control. To avoid the errors in a procedure of interaction, a control model is needed. This paper proposes a systematic strategy with specific algorithms to construct an interactive-control-model based on Petri nets owing to their ability to describe concurrence and other system features. Instead of cumbersome iterations of deadlock detection in the existing studies, this paper introduces the concept of implicit constraints and the related implicit-conflict-marking-search algorithm to excavate them. In the algorithm, only the status of a single robot is needed to analyze the system instead of the status of all system components, which is an important innovation in this paper since this can well help one resolve the state explosion issue. Several examples are provided to show the feasibility of the proposed method. The proposed idea in this paper can be readily applied to practical HCISs. Note to Practitioners—This paper is motivated by the problem of avoiding unexpected situations that may cause industrial accidents in mobile robot systems. Practitioners need to have a control model of the system in order to solve this problem. Existing approaches usually find constraints of a mobile robot system while modeling, which increases the number of iterations. This paper suggests a new approach to find all constraints and then builds a control model for mobile robot systems systematically. With a control model, practitioners can obtain allowed or not allowed operations so as to avoid unexpected situations. Experimental results show the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Activity cycle diagram (ACD), which is essentially a timed Petri net, is one of the oldest formal modeling tools for discrete-event systems, and a flexible manufacturing system (FMS) is a highly automated job shop that is used widely in mechanical and electronics industries. Previous FMS modeling studies have indicated that formal modeling of real-life (or industrial) FMSs with classical ACDs (or Petri nets) is almost impossible. This paper presents an incremental modeling procedure for building a formal simulation model of a real-life FMS with parameterized ACD (P-ACD) that was proposed recently. The incremental modeling procedure consists of job flow modeling, job routing modeling, dispatching rule modeling, and refixture operation modeling. In this paper, a P-ACD model of a real-life FMS was constructed and an FMS simulator was implemented from the proposed P-ACD model. A simulation experiment was conducted in order to demonstrate the usefulness of the FMS simulator. [ABSTRACT FROM PUBLISHER]

In the assembly modeling of complex products, it is not unusual facing an explosive growth of assembly relations, or called combination explosion. Through integrating object-oriented methods with the knowledge template based modeling, in this paper, a new methodology is proposed to deal with the complexity of the assemblies of large-scale products. As such, the templates with various assembly relations can be defined for complex products; constitutive parts or subassemblies in a product structure are defined as object classes; their features can be encapsulated, and defined or modified freely via the parameters of object classes to meet the requirements of a specific instance. The new method supports reusing knowledge and simplifying assembly modeling of complex products. In this paper, recent literatures on object-oriented methodologies and knowledge template methods for assembly modeling are reviewed, the rationales of object-oriented templates are discussed, and a method is proposed for creating assembly templates for complex products. In the case study included in this paper, the developed method has been applied to digital modeling of aero-engines, thus, validating its performance. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

As automation increasingly takes over functions requiring humans’ skill- and rule-based behaviors, effective human-automation interaction increasingly depends upon human reasoning about when the automation should be disengaged. This paper uses the aircraft traffic alert and collision avoidance system (TCAS) as a case study where the pilot needs to decide how to respond to decision automation that generates collision avoidance advisories commanding vertical maneuvers. Of note, recent installations of TCAS can now automatically execute the advisories, potentially to improve compliance with TCAS advisories. This paper describes a Human-in-the-loop flight simulator study examining pilots’ interaction with the automatic execution of advisories, including situations in which pilots chose to disengage it. While overall automatic execution appears to improve compliance to TCAS advisories in the aggregate, it was also disengaged in some of the same situations in which pilots historically had not manually flown compliant avoidance maneuvers. Further, the autopilot’s subsequent reversionary mode settings after following the TCAS advisory may not be appropriate for returning to air traffic clearances. These results highlight the importance of designing automation to support effective pilot reasoning about traffic events, a task that remains in place while the pilot remains ultimately responsible for safety. [ABSTRACT FROM AUTHOR]

Pipe assembly that connects over hundreds of various pipes to mechanical structures is a fundamental challenge to complex product developments. This paper presents a pipe routing algorithm for automatic generation of pipe assembly plans within a constrained space between inner and outer casings of an aeroengine. In this paper, pipes are first ordered in accordance with interference degrees and other engineering rules. Then, a projection-based routing heuristic is developed to find satisfactory collision-free pipe paths in 2D or 3D spaces. Further, the heuristic is modified, by replacing projections with geodesics, to meet requirements of routing pipes on surfaces. Numerical computations of a 119 pipe routing problem are performed to demonstrate the effectiveness and efficiency of the proposed pipe routing algorithm. [ABSTRACT FROM PUBLISHER]

One of the challenges faced by the users of automated visual inspection (AVI) systems is how to efficiently upgrade the legacy systems to inspect the new components introduced into the assembly lines. If the AVI systems are not flexible enough to accommodate new components, they will be rendered obsolete even by small changes in the product being inspected. The overall objective of the research presented in this paper is to produce the methodological basis that will result in the development of highly reconfigurable AVI systems. In this paper, we focus on part of this overall development, the adaptation of preexisting inspection algorithms to inspect similar components introduced into the assembly line. While this paper bases its development and discussion on the inspection of surface mounted devices (SMDs), the proposed methodology is general enough to be applicable to a broad range of inspection problems. Note to Practitioners--In this paper, we present a methodology that would allow the automation of the refinement of AVI algorithms. In particular, the proposed method identifies a set of components, or cluster of components, for which a particular set of inspection features or algorithms, renders a certain level of inspection reliability. This is particularly useful for adapting preexisting systems to inspect new components, especially when the characteristics of the new components are similar to those of components already inspected by the inspection system. We applied this methodology to a case of study of the inspection of SMDs. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

Automation plays an increasingly important role in the global economy. Engineers strive to combine automated devices with mathematical and organizational tools to create complex systems for a rapidly expanding range of applications. Automation is limited only to manufacturing, though manufacturing is a primary application of automation. T-ASE defines automation broadly to include new application areas such as biotechnology, pharmaceutical and health; construction, transportation and security. Research in automation includes many topics at the opertaional enterprise level such as system modeling, analysis, performance evaluation.

The IEEE Transactions on Automation Science and Engineering (T-ASE) wishes to thank the 1110 reviewers over the past year who have performed an essential role in maintaining the quality of this publication. T-ASE strives for the 90-90 standard, i.e., 90% of articles should be reviewed within 90 days of submission. The review process starts when the Editor-in-Chief assigns the paper to an Editor, who assigns it to an Associate Editor (AE). The AE then obtains confirmation from three to five reviewers, carefully studies the results, and makes a recommendation to the Editor, who then makes a decision that is reported back to the lead author. This review process is very demanding, especially under time constraints. We have summarized the process under “Guidelines for T-ASE Editors and AEs for Handling Papers” on our website. I am pleased to report that submissions have steadily increased (over 1000 in 2019) and our impact factor is now 5.224, a 42.5% increase over the past year. As we strive to increase the quality and impact of the journal, we rely on the hard work of our reviewers and members of the Editorial Board. To those dedicated volunteers, and to anyone who may have been inadvertently omitted from the following list, we extend our sincere appreciation. [ABSTRACT FROM AUTHOR]