Robotic Workcell Research Articles

Toward a digital twin of a robot workcell: standards and methods

Digital twins are poised to improve the engineering, operation, and management of manufacturing facilities. However, implementing digital twins has been a challenge primarily due to a lack of resources and the absence of standardized digital twin frameworks and methods. This paper describes creating a digital twin of a robot workcell using available standards, tools, and methods. The workcell comprises collaborative robot arms, a computer numeric control (CNC) machine tool, and a coordinate measuring machine (CMM). The ISO 23247 standard provides a guideline for building the digital twin. Data are collected from physical devices and the MTConnect standard provides a format for communicating the data between the physical equipment and their digital counterparts. The machine components in the workcell are represented as models in the virtual world. This research shows that the above standards and methods support building a digital twin of a robot workcell. The data collected and communicated can be used to improve workcell functions such as quality management and predictive maintenance.

Robotical Automation in CNC Machine Tools: A Review

Abstract Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

Robot Base Placement Optimization for Pick-and-Place Sequences in Industrial Environments

This work proposes a highly parallelized and efficient optimization procedure for finding the globally optimal robot base placement in industrial assembly tasks. This procedure fully embeds a point-to-point (P2P) trajectory planner to evaluate the placements, considering kinodynamic robot limits, collisions, robot configuration changes, and a cyclic constraint. The investigated optimization goals are cycle-time optimality, time-energy optimality, and adaptivity to dynamically changing throughput. The latter uses an optimization-based method to scale the cycle-time-optimal trajectory in low-throughput situations to reduce energy consumption. The approach is used to optimize the base placement in two robotic work cells, showing a reduction in the cycle time of up to 44% and a reduction of energy consumption of up to 53% in a best-to-worst comparison.

Development of an Integrated Robotic Workcell for Automated Bonding in Footwear Manufacturing

Development of an Integrated Robotic Workcell for Automated Bonding in Footwear Manufacturing

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases.

Improving Robotic Bin-Picking Performances through Human–Robot Collaboration

The automation of bin-picking processes has been a research topic for almost two decades. General-purpose equipment, however, still does not show adequate success rates to find application in most industrial tasks. Human–robot collaboration in bin–picking tasks can increase the success rate by exploiting human perception and handling skills and the robot ability to perform repetitive tasks. The aim of this paper, starting from a general-purpose industrial bin picking equipment comprising a 3D–structured light vision system and a collaborative robot, consists in enhancing its performance and possible applications through human–robot collaboration. To achieve successful and fluent human–robot collaboration, the robotic workcell must meet some hardware and software requirements that are defined below. The proposed strategy is tested in some sample tests: the results of the experimental tests show that collaborative functions can be particularly useful to overcome typical bin picking failures and to improve the fault tolerance of the system, increasing its flexibility and reducing downtimes.

Automated Configuration of Heterogeneous Graph Neural Networks With a Semantic Math Parser for IoT Systems

Efficient training of deep learning models from time series data for Internet of Things (IoT) systems requires a good understanding of the domain, particularly if the training is automated for large-scale applications. Heterogeneous graph neural networks (HGNNs) are a promising approach for incorporating domain knowledge into the modeling framework and consequently improving model performance. However, encoding domain knowledge into HGNNs is nontrivial for IoT systems and requires substantial manual effort. This complicates the adoption of HGNNs in practical settings. To overcome this drawback, we propose a framework for the automatic derivation of HGNN features by semantically parsing equations present in scientific and dedicated publications. We encode the derived features considering physical causation from these equations into an HGNN using an underlying Transformer for prediction and anomaly detection. We validate our approach using two IoT use cases, namely, the prediction of the remaining energy in the battery of an electric race car and the anomaly detection during pick and place operations in a robot workcell. We demonstrate that our approach significantly outperforms other competitive techniques.

Digital twin design and analytics for scaling up electric vehicle battery production using robots

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation.

Hybrid additive robotic workcell for autonomous fabrication of mechatronic systems - A case study of drone fabrication

• A hybrid additive manufacturing / pick-n-place robotic workcell enables autonomous, flexible fabrication of full-scale functional mechatronic systems. • A modular electronics concept is demonstrated to enable error-free autonomous, robotic construction of full-scale mechatronic sub-systems. • The workcell's functionality is verified through the back-to-back manufacture of different drone designs that are able to fly directly from the build platform. • Functional drones are rapidly manufactured at the point of need in under four hours This paper presents the realization of a robotic workcell capable of autonomous, flexible, fabrication of complete, full-scale, functional mechatronic systems. Using a 6-DOF robotic arm with a hybrid additive manufacturing (AM)/pick-and-place (PnP) end-effector, the workcell can rapidly print enclosures, insert and wire functional electronics, and embed them into the printed structure. The workcell's scalable control system, digital workflow for creating assembly instructions, hybrid AM/PnP toolhead design, and autonomous part tending mechanism are detailed. In addition, a modular electronics infrastructure is presented to enable facile, error-free robotic wiring of large-gauge copper wire. The workcell's ability to manufacture is verified in a case study wherein it is used to autonomously fabricate a variety of Unmanned Aerial Systems (UAS). In the study, the workcell produces a functional quadrotor drone, releases the drone from the workcell, and allows the drone to record the workcell constructing a second, different drone - all without human involvement. The resulting workcell, which combines digitally-integrated manufacturing, robotics and automation, and hybrid process manufacturing, enables autonomous manufacture of complete, functional mechatronic systems that are tailored to custom design needs in a just-in-time manner.

Optimal design of robotic work-cell through hierarchical manipulability maximization

The increasing requests for flexible robotic applications involving the rapid relocation of the robot manipulator , possibly mounted on a mobile base, imposes tolerance to imprecise positioning. The high manipulability of the nominally designed poses, i.e. , the capacity to change the position and the orientation of a given robot joint configuration’s end-effector, is often considered a proxy for robustness to imprecise positioning. This work presents a method for choosing target end-effector poses to manipulate bulky objects in complex environments. The paper proposes a two-layer optimizer connected in cascade to maximize the manipulability and achieve reasonable computational time. First, using a genetic algorithm (GA) allows a global search for a satisfactory solution to the target poses of the task at the same time. Subsequently, the output of the GA becomes the initial guess for the simulated annealing (SA) algorithm, which locally maximizes each pose’s manipulability separately. The feasibility of the connecting trajectories and collisions are checked in both layers. Experiments show the method’s ability to find excellent solutions within a limited time, considering a complex problem involving manipulating large objects in a cluttered environment. The simulations of three working scenarios allowed testing of the proposed method. The final validation of the algorithm was on two relevant industrial use-cases: the manipulation of sidewalls and the manipulation of cargo panels inside an aircraft fuselage . • Optimal robotic workcell design can be a hard problem in some situations. • A tool is provided to optimize the robot position with respect to target poses. • Cascaded optimization allows for fast solutions search in complex environments. • Global optimization of the whole set of tasks followed by single task refinement.

Auto-calibration of robot workcells via remote laser scanning

• The remote robot calibration method that requires neither marker settings nor initial positioning of the measurement system was proposed. • The interplane relation feature extracted from point-cloud data was used to identify the relative differences between the coordinates of the 3D model and those of the real workcell. • We presented an iterative method to estimate world coordinates and increase the accuracy of the revised robot path. • The accuracy of adjusted robot program is measured to be 0.0817 mm based on a test of the pointing operation. For new robotic products, the rapid generation of robot programs is generally ensured by offline programming (OLP). Nevertheless, robotic calibration of workcells is essential because of the geometric differences between the three-dimensional (3D) model environment and the real world. In contrast with previous robot self-calibration methods that use specific sensors, the proposed method requires neither marker settings nor initial positioning of the measurement system. Instead, the geometric feature of interplane relations finds relative differences between the coordinates of the 3D model and those of the real workcell. In addition, an iterative method of estimating world coordinates is introduced to increase the accuracy of the revised robot path. A portable laser scanner acquires 3D point-cloud data, so it can be located near the workcell instead of being pre-fixed, thereby facilitating its use. Through experimentation, an average distance of 0.0817 mm is found between the OLP-teaching points and the calibrated points of the pointing operation.

Artificial Intelligence in Foreign Object Classification in Fenceless Robotic Work Cells Using 2-D Safety Cameras

Artificial Intelligence in Foreign Object Classification in Fenceless Robotic Work Cells Using 2-D Safety Cameras

Wireless Time Sensitive Networking Impact on an Industrial Collaborative Robotic Workcell

In this article, we describe a methodology and associated models to evaluate a time sensitive collaborative robotics application enabled by wireless time sensitive networking (WTSN) capabilities. We also present a method to configure WTSN scheduling to meet the application time budget and validate it in a realistic industrial use case. We detail the methodologies for implementing and characterizing the performance of key WTSN capabilities, namely time synchronization and time-aware scheduling, over an IEEE 802.11 based network. We deploy the WTSN capabilities with a collaborative robotic workcell consisting of two robotic arms, which emulate a material handling application, known as machine tending. We further explore configurations and measurement methodologies to characterize application performance of this use case and correlate it to the performance of the wireless network.

Thermal modeling and uncertainty quantification of tool for automated garment assembly

In this work, a thermal Finite Element model is developed to simulate the performance of a blade-like tool for robotic work cells performing automated garment production using a novel thermoplastic stiffening layer. Uncertainty quantification and sensitivity analysis are applied to determine the most important design properties and optimize key performance metrics for swift and reliable garment assembly. Attention is focused on the geometric and thermal design properties that minimize sensitivity to environmental conditions while maximizing expected productivity. An example design is shown for illustrative purposes. This work may inform future design innovation for similar heating tools and reduce the need for physical experiments and long calibration times on the factory floor.

Planning Collision-Free Robot Motions in a Human–Robot Shared Workspace via Mixed Reality and Sensor-Fusion Skeleton Tracking

The paper describes a method for planning collision-free motions of an industrial manipulator that shares the workspace with human operators during a human–robot collaborative application with strict safety requirements. The proposed workflow exploits the advantages of mixed reality to insert real entities into a virtual scene, wherein the robot control command is computed and validated by simulating robot motions without risks for the human. The proposed motion planner relies on a sensor-fusion algorithm that improves the 3D perception of the humans inside the robot workspace. Such an algorithm merges the estimations of the pose of the human bones reconstructed by means of a pointcloud-based skeleton tracking algorithm with the orientation data acquired from wearable inertial measurement units (IMUs) supposed to be fixed to the human bones. The algorithm provides a final reconstruction of the position and of the orientation of the human bones that can be used to include the human in the virtual simulation of the robotic workcell. A dynamic motion-planning algorithm can be processed within such a mixed-reality environment, allowing the computation of a collision-free joint velocity command for the real robot.

Environmental Monitoring for Closed Robotic Workcells Used in Aseptic Processing: Data to Support Advanced Environmental Monitoring Strategies

The human operator is acknowledged as the greatest potential source of contamination in aseptic processing. To avoid contamination, barrier systems have progressively reduced the amount of human intervention in the critical zone. This study extends the trajectory of enhanced patient safety through the elimination of human intervention in aseptic filling. Eight companies that are users of closed robotic workcells have aggregated their usage data from 2018 to 2021. The study analyzes the critical design elements and performance of the Cytiva SA25 Aseptic Filling Workcell. The SA25 is a standardized, fully closed robotic system for aseptic filling of vials, syringes, and cartridges that eliminates operator intervention in the critical zone. The standardized design means that the system is not modified to suit a particular application and the same environmental monitoring strategy can be used across different installations. The SA25 provides significantly increased sterility assurance when producing sterile injectables. Users have observed non-viable particle levels well within ISO 5/Grade A air requirements, with extremely low probabilities of entering a dosage container. There have been zero cases of microbial growth in more than 250 media fills and good manufacturing practice (GMP) batches. Across all dosage formats and sizes, the aseptic process is repeatable, with more than 99.3% of units meeting acceptance criteria. These data demonstrate that eliminating risk through design is successful in the SA25, with an improved aseptic process in comparison to filling systems using Restricted Access Barrier Systems (RABS) or isolators with glove ports. One of the contributing companies to this article achieved an industry first. The U.S. FDA-approved commercial production of their biologic drugs without the requirement for routine viable environmental monitoring (EM), requiring viable EM only during process simulations. Based on the data presented and planned future research, new regulatory consideration should be made for closed robotic workcells to ensure that regulations meant for previous technologies with different risk profiles are not inappropriately applied.Graphical abstract

A Virtual Element-Based Postural Optimization Method for Improved Ergonomics During Human-Robot Collaboration

Human-robot collaboration is becoming increasingly popular in the manufacturing industry, opening the door to a large range of applications by combining the complementary skills of the human worker and the robot. Collaborative robots are also a solution to decrease the operator workload and indirectly reduce the risk of occupational injuries such as musculoskeletal disorders (MSDs). The latter represents one of the major causes of absenteeism at work. Thanks to the development of human tracking devices, it is possible to monitor the operator, analyze the postures, and assess the associated MSD risk. In this paper, we present a novel ergonomics optimization framework that performs postural optimization based on the virtual element method. A feedback interface is developed whereby the user is informed about non-ergonomic postures and an improved body pose is proposed. The workpiece position controller module acts on the cobot end-effector and indirectly on the co-manipulated part in such a way that the operator’s posture is improved. The framework was validated by a user study performed on a human-robot collaboration task whereby the subject polishes a part hold by the robot. The conducted study of the user’s perception and REBA scores showed promising results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of non-ergonomic posture of workers in hybrid workcells. The proposed approach makes use of virtual elements (springs and dampers) to build a mechanical model of the human body posture and perform postural optimization. The obtained body joint angles are fed into two modules of the framework. First, a graphical interface displays the current pose of the user and proposes to him the improved posture. Second, a controller adapts the pose of the workpiece hold by the collaborative robot. This is realized by computing a displacement vector between the wrist current and optimized positions. The use of such a framework was demonstrated on a collaborative polishing task whereby the robot adjusts the position of the workpiece. After a user study test with 10 participants, joint data were collected and the REBA scores of different subtasks were measured and compared. The results from these preliminary experiments showed that the proposed approach improves the human body postures and offers a promising solution to enhance ergonomics by the robot assistance in case of robotic workcells. The conducted survey also shows an overall positive subject’s perception of the system.

Correction to: Concurrent layout and trajectory optimization for robot workcell toward energy‑efficient and collision‑free automation

Correction to: Concurrent layout and trajectory optimization for robot workcell toward energy‑efficient and collision‑free automation

Concurrent layout and trajectory optimization for robot workcell toward energy-efficient and collision-free automation

Concurrent layout and trajectory optimization for robot workcell toward energy-efficient and collision-free automation

Influential Aspects to Robotic Cell Energetic Efficiency: Overview

Abstract This paper focuses on the input parameters that affect the resulting efficiency of a robotic workplace for industrial use. The aim of the investigation was to find which parameters most significantly affect cycle time and electricity consumption, and which way of movement is the most suitable for robotic operation. By finding the most suitable parameters, we obtain the information on how to build the most efficient robotic workplace. The investigation focused mainly on the energy consumption and layout of the robotic work cells components that significantly affect the cycle time and potentially save electric energy.