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Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of 3.23 % for the RGB-D camera-based skeleton detection system, against an NRMSE of 12.23 % for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged 208 mm , being 200 mm the limit safety distance set by algorithm. [ABSTRACT FROM AUTHOR]

Collaborative robotics is a field of growing industrial interest, within which understanding the energetic behavior of manipulators is essential. In this work, we present the electro-mechanical modeling of the UR5 e-series robot through the identification of its dynamics and electrical parameters. By means of the identified robot model, it is then possible to compute and optimize the energy consumption of the robot during prescribed trajectories. The proposed model is derived from data acquired from the robot controller during bespoke experimental tests, using model identification procedures and datasheet provided by manipulator, motors, and gearbox manufacturers. The entire procedure does not require the use of any additional sensor, so it can be easily replicated with an off-the-shelf manipulator, and applied to other robots of the same family. [ABSTRACT FROM AUTHOR]

This paper explores and experimentally compares the effectiveness of robot-stopping approaches based on the speed and separation monitoring for improving fluency in collaborative robotics. In the compared approaches, a supervisory controller checks the distance between the bounding volumes enclosing human operator and robot and prevents potential collisions by determining the robot's stop time and triggering a stop trajectory if necessary. The methods are tested on a Franka Emika robot with 7 degrees of freedom, involving 27 volunteer participants, who are asked to walk along assigned paths to cyclically intrude the robot workspace, while the manipulator is working. The experimental results show that scaling online the dynamic safety zones is beneficial for improving fluency of human-robot collaboration, showing significant statistical differences with respect to alternative approaches. [ABSTRACT FROM AUTHOR]

Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors' performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator's poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance. [ABSTRACT FROM AUTHOR]

Recent advances in the field of collaborative robotics aim to endow industrial robots with prediction and anticipation abilities. In many shared tasks, the robot's ability to accurately perceive and recognize the objects being manipulated by the human operator is crucial to make predictions about the operator's intentions. In this context, this paper proposes a novel learning-based framework to enable an assistive robot to recognize the object grasped by the human operator based on the pattern of the hand and finger joints. The framework combines the strengths of the commonly available software MediaPipe in detecting hand landmarks in an RGB image with a deep multi-class classifier that predicts the manipulated object from the extracted keypoints. This study focuses on the comparison between two deep architectures, a convolutional neural network and a transformer, in terms of prediction accuracy, precision, recall and F1-score. We test the performance of the recognition system on a new dataset collected with different users and in different sessions. The results demonstrate the effectiveness of the proposed methods, while providing valuable insights into the factors that limit the generalization ability of the models. [ABSTRACT FROM AUTHOR]

This report summarizes the recent research projects carried out at the Collaborative Robotics and Adaptive Machines Laboratory, Old Dominion University. The topics include collaborative robotics strategies for non-repetitive micro-drilling tasks, hand-held robots for spillage-free specimen retrieval tasks, development of a three-dimensional simulator for autonomous underwater vehicles, and enhancing search efficiency of glowworm-inspired robotic swarms for multiple source localization tasks. Research in these areas has applications in autonomous systems, advanced manufacturing, and health care. [ABSTRACT FROM AUTHOR]

The advent of collaborative robotics has enabled humans and robots to collaborate closely in carrying out manufacturing activities. Together, they can leverage their unique strengths and capabilities to tackle complex tasks. This partnership between humans and robots is often described as symbiotic in literature, but this concept is frequently oversimplified to a simple exchange of mutual benefits. In reality, symbiosis encompasses a wide range of interactions, some of which may be beneficial while others might be detrimental. To effectively manage Human–Robot Symbiosis, it is important to understand its underlying principles. In this view, this paper has two main objectives: (i) to reinterpret collaborative tasks in assembly processes based on the characteristics of symbiotic relationships; and (ii) to propose a new approach for evaluating assembly tasks inspired by the bio-inspired features of collaborative human–robot systems. Overall, the results of this study represent a step towards achieving a deeper understanding of the principles of Human–Robot Symbiosis, useful to develop effective solutions for enhancing collaboration between humans and robots in assembly processes. [ABSTRACT FROM AUTHOR]

One of the most interesting characteristics of collaborative robots is their ability to be used in close cooperation scenarios. In industry, this facilitates the implementation of human-in-loop workflows. However, this feature can also be exploited in different fields, such as healthcare. In this paper, a rehabilitation framework for the upper limbs of neurological patients is presented, consisting of a collaborative robot that helps users perform three-dimensional trajectories. Such a practice is aimed at improving the coordination of patients by guiding their motions in a preferred direction. We present the mechatronic setup, along with a preliminary experimental set of results from 19 volunteers (patients and control subjects) who provided positive feedback on the training experience (52% of the subjects would return and 44% enjoyed performing the exercise). Patients were able to execute the exercise, with a maximum deviation from the trajectory of 16 mm. The muscular effort required was limited, with average maximum forces recorded at around 50 N. [ABSTRACT FROM AUTHOR]

In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human–robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human–robot interactive systems. [ABSTRACT FROM AUTHOR]

The goal of this article is to propose a cross-perspective around Collaborative Robotics—seen as a remarkable example of technologies 4.0 in an industrial context—by calling on sociology, activity-centred ergonomics, engineering, and robotics expertises. The development of this cross-perspective is thought to be a key issue to improve the design of work organisation for the Industry 4.0. After a socio-historical review of promises of Collaborative Robotics, the interdisciplinary approach developed and applied in a French Small & Medium Enterprise (SME) is presented. In this case study, two work situations are focused on in an interdisciplinary perspective: on the one hand, the one of operators whose professional gestures are intended to be supported by collaborative robots, and on the other the one of managers and executives as responsible for socio-technical changes. Our results reveal the technical and socio-organisational challenges faced by SMEs beyond the introduction of given technologies: analysing the relevance and feasibility of cobotisation projects with regard to the complexity of professional gestures and preserving the quality of work and performance under a continuous pressure to change (organisations, technologies). These findings support discussions of promises of collaborative robotics, and more generally Industry 4.0, regarding effective worker/technology collaboration and the possibility of "healthy" and performant work; they reiterate requirements for work-centred and participatory design, for reconnection in a sensory experience in a more and more digitalized work and open ways for more interdisciplinary approaches. [ABSTRACT FROM AUTHOR]

In assembly processes, collaborative robots (cobots) can provide valuable support to improve production performance (assembly time, product quality, worker wellbeing). However, there is a lack of models capable of evaluating cobot deployment and driving decision-makers to choose the most cost-effective assembly configuration. This paper tries to address this gap by proposing a novel cost model to evaluate and predict assembly costs. The model allows a practical and straightforward comparison of different potential assembly configurations in order to guide the selection towards the most effective one. The proposed cost model considers several cost dimensions, including manufacturing, setup, prospective, retrospective, product quality and wellbeing costs. The cost estimation also considers learning effects on assembly time and quality, particularly relevant in low-volume and mass customised productions. Three real manufacturing case studies accompany the description of the model. [ABSTRACT FROM AUTHOR]

The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz, utilizing an external off-the-shelf force–torque (FT) sensor. The proposed work is demonstrated on a 500 mm2 near-net-shaped Wire–Arc Additive Manufacturing (WAAM) complex component with embedded defects by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting in the uniform coupling of ultrasound inspection. The utilized method proves superior in performance and speed, accelerating the programming time using online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources, and achieving flexible production. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots (cobots) are known for their ability to operate in dynamic environments to perform many different tasks (since they can be easily reprogrammed). Due to their features, they are largely used in flexible manufacturing processes. Since fault diagnosis methods are generally applied to systems where the working conditions are bounded, problems arise when defining condition monitoring architecture, in terms of setting absolute criteria for fault analysis and interpreting the meanings of detected values since working conditions may vary. The same cobot can be easily programmed to accomplish more than three or four tasks in a single working day. The extreme versatility of their use complicates the definition of strategies for detecting abnormal behavior. This is because any variation in working conditions can result in a different distribution of the acquired data stream. This phenomenon can be viewed as concept drift (CD). CD is defined as the change in data distribution that occurs in dynamically changing and nonstationary systems. Therefore, in this work, we propose an unsupervised anomaly detection (UAD) method that is capable of operating under CD. This solution aims to identify data changes coming from different working conditions (the concept drift) or a system degradation (failure) and, at the same time, can distinguish between the two cases. Additionally, once a concept drift is detected, the model can be adapted to the new conditions, thereby avoiding misinterpretation of the data. This paper concludes with a proof of concept (POC) that tests the proposed method on an industrial collaborative robot. [ABSTRACT FROM AUTHOR]

Designing product families is an enabling strategy for mass customization. In general, there are four prevalent classes of problems when designing product families: (i) product family positioning; (ii) customer preferences modeling; (iii) product family modeling; and (iv) product family configuration. Although these classes are interwoven through design problems stemming from marketing, engineering, and economic areas, they are rarely handled together in product family design methods. The lack of a systemic, integrated design perspective may lead to locally optimal solutions and ultimately result in product families not making the economic benefits of customization worthwhile. Over the years, some methods have attempted to overcome this absence of holistic design view. However, because they are restricted to theoretical levels or lack detailed applications, their practical implementation is often not possible. To bridge the pathway between theory and practical implementation, this paper uses the market-driven modularity (MDM) method to design a family of autonomous mobile palletizers economically oriented to market requirements. The empirical application of the method points out the palletizers family as being economically feasible. Furthermore, it also indicates which modules should be developed in successive design phases, as well as reveal the definition of the product family structure as the MDM's outcome that is more sensitive to the variation of parameters/variables composing the configuration model. The main contribution of this work lies in the presentation of practical implementation details of the MDM method, which, to the best of our knowledge, has not been reported since its proposition. [ABSTRACT FROM AUTHOR]

The key concept of collaborative robotics is represented by the presence of a strict interaction between a human user and the robotic system. As such, the study of the interaction is of paramount importance for a successful implementation of the system. In this article, we propose a novel approach to address the problem of designing a collaborative robotic system for industrial applications, focusing on the characteristics of the interaction. In particular, we will propose a set of methodologies focused on interaction design, inspired by those used for the design of user interfaces. These methodologies will allow the design of collaborative robotic systems following a user-centered approach, thus putting emphasis not only on safety and adaptability of the robotic systems (which have been widely addressed in the literature), but also on the interaction experience. While the proposed methodology was developed considering general collaborative robotics applications, two real industrial case studies were considered, to instantiate the considered framework and showcase its applicability to the real-world domain. Note to Practitioners—This article aims at bridging the gap between interaction design and collaborative robotics. In particular, the proposed methodology will represent a toolset for robotic experts (researchers and system integrators), for understanding the user experience and designing the robotic system ensuring an effective interaction. In fact, while robotics experts are typically well aware of issues and methodologies related to technological and application aspects, they often tend to ignore the principles of interaction. Such principles are commonly adopted in the design of computer-based human–machine interfaces or web applications, but, to the best of the authors’ knowledge, have never been applied to the design of collaborative robotic systems for industrial applications. Hence, this article will serve as a fundamental step to bring interaction design principles into the robot integration domain. [ABSTRACT FROM AUTHOR]

Industrial collaborative robotics is promising for manufacturing activities where the presence of a robot alongside a human operator can improve operator's working conditions, flexibility, and productivity. A collaborative robotic application has to guarantee not only safety of the human operator, but also fluency in the collaboration, as well as performance in terms of productivity and task time. In this paper, we present an approach to enhance fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones. A supervisory controller runs online safety checks between bounding volumes enclosing robot and human to identify possible collision dangers. To optimize the sizes of safety zones enclosing the manipulator, the method minimizes the time of potential stop trajectories considering the robot dynamics and its torque constraints, and leverages the directed speed of the robot parts with respect to the human. Simulations and experimental tests on a seven-degree-of-freedom robotic arm verify the effectiveness of the proposed approach, and collaborative fluency metrics show the benefits of the method with respect to existing approaches. [ABSTRACT FROM AUTHOR]

This paper studies the dual-arm manipulation of an object by means of two collaborative robots. The latter hold the object through limited contact areas, thus applying unilateral contact constraints. This manipulation strategy increases versatility, since it does not require specific grippers depending on the object shape and size. However, to ensure grasping stability (i.e. no slipping of the object), a suitable internal force must be prescribed to ensure the fulfillment of the static-friction condition. In this work, the trend of the internal force is included among the inputs of a time-optimal trajectory planning, in order to find the minimal internal prestress that is able to both satisfy the static-friction condition and manipulate the object in minimal time. Admittance control is used to modulate the forces exerted by the robot end-effectors on the object. An extensive experimentation, on different 6-dimensional trajectories reaching linear and angular accelerations up to 4.5 m/s2 and 7.4 rad/s2, is presented and discussed. • Unilateral cooperative grasping and admittance control for dual-arm manipulation. • Trajectory planning based on optimal force application and minimal time execution. • The applied internal force is optimized to ensure grasping stability. • 6D trajectories of the manipulated object reaching maximum accelerations up to 4.5 m/s2 and 7.4 rad/s2. • Wide experimental campaign with thorough force analysis. [ABSTRACT FROM AUTHOR]

The paper deals with a lead-through method of programming for industrial robots. The goal is to automatically reproduce 6DoF trajectories of a tool wielded by a human operator demonstrating a motion task. We present a novel motion-tracking system built around the HTC Vive pose estimation system. Our solution allows complete automation of the robot teaching process. Specific algorithmic issues of system calibration and motion data post-processing are also discussed, constituting the paper's theoretical contribution. The motion tracking system is successfully deployed in a pilot application of robot-assisted spray painting. [ABSTRACT FROM AUTHOR]

Human-robot collaboration (HRC), where humans and robots work together to handle specific tasks, requires designing robots that can effectively support human beings. Robots need to conduct reasoning using commonsense knowledge (CSK), e.g., fundamental knowledge that humans possess and use subconsciously, in order to assist humans in challenging and dynamic environments. Currently, there are several effective CSK systems used for organizing information and facts, along with detecting objects and determining their properties. HRC is employed in various manufacturing tasks, such as paint spraying and assembly, in order to keep humans safe while increasing efficiency. Although there is a large array of research on HRC and on CSK, there is minimal research linking the two concepts together. This paper presents a novel system on human-robot collaboration guided by commonsense reasoning for automation in manufacturing tasks. This fits within the general realm of smart manufacturing. The primary focus is on improving the efficacy of human-robot co-assembly tasks. Evaluations conducted with online simulations and real-world experiments indicate that reasoning using CSK-based robot priorities enhances HRC as compared to simpler robot priorities, e.g., merely handling nearby objects. This system is modifiable and can be used for larger and more complex real-world tasks, thereby leading to improved automation in manufacturing. This paper demonstrates the scope of combining HRC and CSK, while future works will be able to further utilize the benefits of combining the two fields with significant impacts. Note to Practitioners—This paper is motivated by the human-robot collaboration problem in smart manufacturing. Robots operating by reasoning with commonsense priorities in human-robot collaboration enable faster task execution and better human work life. This can help balance work for humans and prevent injury. Adding robots to tasks accordingly does not necessarily decrease costs, but can limit human exposure to danger which is significant (and can also lower costs overall). Simulations and real-world experiments in our research using commonsense reasoning demonstrate how work is easier and better with human-robot collaboration. These factors are highly significant when tasks are repeated multiple times. The system is presented within automated manufacturing and is scalable for different real-world applications. Such automation is particularly helpful during recent times in the aftermath of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario. The key enabling technology that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees’ safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution. Note to Practitioners—The design and development of manufacturing systems are experiencing substantial changes towards full automation. This ongoing challenge is being tackled by academia and industrial practitioners with the adoption of collaborative robots, where the skills and peculiarities of humans (e.g., intelligence, creativity, adaptability, etc.) and robots (e.g., flexibility, pinpoint accuracy, tirelessness, etc.) are combined to better perform a variety of tasks. Nevertheless, due to their different characteristics, there is an emerging need for designing suitable decision and control techniques to ensure a safe and ergonomic HRC, while keeping the highest level of productivity. Against this background, the aim of this paper is to provide researchers and practitioners with a reference source in the related field, which can help them designing and developing suitable solutions to control problems in safe, ergonomic, and efficient collaborative robotics. [ABSTRACT FROM AUTHOR]

This article presents a computed-torque impedance control architecture for robotic-assisted mold polishing based on human skills.The mold polishing process for plastic injection is critical since tiny surface imperfections are easily visible in the plastic product, having strong implications in the overall quality. A Cartesian impedance control referred to the end-effector frame with posture optimization is proposed to tackle this problem. A collaborative robot manipulator from Franka Emika and a steel mold are used in the experiments to validate our approach. Human skills are captured both in comanipulation and free-hand modes, where position and force patterns are analyzed. Those patterns are then autonomously replicated by the robotic system, validating the proposed methodology. [ABSTRACT FROM AUTHOR]

Introduction: The ROSA (Robotic Surgical Assistant) Knee system (Zimmer Biomet, Warsaw, IN) for total knee arthroplasty (TKA) can be considered as collaborative robotics, where the surgeon remains in charge of the procedure and collaborates with a smart robotic tool, to perform the surgery with a high accuracy and reproducibility. The aim was to describe: (1) its concept and surgical technique; (2) its advantages and potential limits; (3) the early experience with this system. Materials and methods: The goal during its development phase was to keep the surgeon active and at the center of the operation: the surgeon handles the saw and performs the cuts while the robotic arm places and holds the guide at the right place. The ROSA knee platform assists the surgeon for the distal femoral cut, the femoral component sizing and positioning, the tibial cut and the ligament balance. This robotic system has two options: image-based with 3D virtual model; or image-less, based on intraoperative landmarks acquisition. All the classic surgical techniques can be used: measured resection, gap balancing, functional alignment, kinematic alignment. Some techniques recently developed are more ROSA-specific: Robotic personalized TKA, ROSA-FuZion technique. Results: Its advantages as compared to other available systems include: radiographs in standing position, collaborative robotic system where the robot completes the surgeon skills, "off-the-shelf" implants, predictive robotic with concept of machine learning incorporated into the system. Two cadaveric studies have reported the high accuracy and reproducibility of this device. This robotic system is recent and currently no clinical series has enough follow-up to report clinical outcomes. Conclusion: The ROSA knee system is a robotically assisted semi-autonomous surgical system with some specific characteristics. The aim of this collaborative robotic system is to improve the accuracy and reliability of the bone resections and the ligament balancing, without replacing the steps well performed by the surgeon. [ABSTRACT FROM AUTHOR]

Within the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and collaborative assembly applications by focusing on the main features that have positively influenced workers' cognitive responses. A methodology for structured development has been proposed. The draft guidelines have been created starting from the outcomes of a systematic and extended screening of the scientific literature. Preliminary validation has been carried out with the help of researchers working in the field. Inputs on comprehensibility and relevance have been gathered to enhance the guidelines. Lastly, a survey was used to examine in depth how international experts in different branches can interpret such guidelines. In total, 108 responders were asked to qualitatively and quantitatively evaluate the guideline's comprehensibility and provide general comments or suggestions for each guideline. Based on the survey's results, the guidelines have been validated and some have been reviewed and re-written in their final form. The present work highlights that integrating human factors into the design of collaborative applications can significantly bolster manufacturing operations' resilience through inclusivity and system adaptability by enhancing worker safety, ergonomics, and wellbeing. • Industry 5.0 manufacturing systems must adapt to workers requirements and needs. • A methodology for developing and validating guidelines for designing human-centred collaborative applications is proposed. • Guidelines should aid individuals without expertise in human factors to design anthropocentric Human-Robot Interaction. • An interdisciplinary research team developed, revised, updated, and validated the guidelines by means of an expert survey. [ABSTRACT FROM AUTHOR]

For remaining competitive in the current industrial manufacturing markets, coating companies need to implement flexible production systems for dealing with mass customization and mass production workflows. The introduction of robotic manipulators capable of mimicking with accuracy the motions executed by highly skilled technicians is an important factor in enabling coating companies to cope with high customization. However, there are some limitations associated with the usage of a fully automated system for coating applications, especially when considering customized products of large dimensions and complex geometry. This paper addresses the development of a collaborative coating cell to increase the flexibility and efficiency of coating processes. The robot trajectory is taught with an intuitive programming by demonstration system, in which an icosahedron marker with multicoloured LEDs is attached to the coating tool for tracking its trajectories using a stereoscopic vision system. For avoiding the construction of fixtures and allowing the operator to freely place products within the coating work cell, a modular 3D perception system was developed, relying on principal component analysis for performing the initial point cloud alignment and on the iterative closest point algorithm for 6 DoF pose estimation. Furthermore, to enable safe and intuitive human-robot collaboration, a non-intrusive zone monitoring safety system was employed to track the position of the operator in the cell. [ABSTRACT FROM AUTHOR]

Industrial collaborative robotics is an enabling technology and one of the main drivers of Industry 4.0 in industrial assembly. It allows a safe physical and human-machine interaction with the aim of improving flexibility, operator's work conditions, and process performance at the same time. In this regard, collaborative assembly is one of the most interesting and useful applications of human-robot collaboration. Most of these systems arise from the re-design of existing manual assembly workstations. As a consequence, manufacturing companies need support for an efficient implementation of these systems. This work presents a systematical methodology for the design of human-centered and collaborative assembly systems starting from manual assembly workstations. In particular, it proposes a method for task scheduling identifying the optimal assembly cycle by considering the product and process main features as well as a given task allocation between the human and the robot. The use of the proposed methodology has been tested and validated in an industrial case study related to the assembly of a touch-screen cash register. Results show how the new assembly cycle allows a remarkable time reduction with respect to the manual cycle and a promising value in terms of payback period. [ABSTRACT FROM AUTHOR]

An emerging scientific field is the study of safety and ergonomics in the agricultural sector during human-robot interaction. Human-robotic synergetic systems are considered to be the most mature way to circumvent problems appearing due to the complex and unpredictable nature of the agricultural environment, which contrasts with the stable domain found in industrial settings. In promising working ecosystems, the distinctive cognitive human characteristics of perception, decision making and acting can be combined with the strength and repeatable accuracy of robots. However, safety must be guaranteed both in terms of avoiding accidents during unwanted physical contacts and provoking musculoskeletal disorders. The latter is a concise term for describing numerous soft tissues disorders, which have reached epidemic proportions among farmers undermining their quality of life. This investigation, by describing the fundamentals of human-robot interaction from an agriculture-oriented perspective, methodically tries to identify potential hazards that can put human safety at risk. In order to overcome these hazards, approaches for minimising the occurrence of injuries analysed along with methods for safe collaboration. The innovation of this study lies on focusing on ergonomics during agricultural human-robot interactive operations. Thus, through reviewing the basic ergonomic principles and the main risk factors, potential challenges are captured concerning human factors, technologies and policy directions. Ensuring of safety in this kind of systems should have a positive impact in technological, societal and economic aspects. For this purpose, an intensive effort and interdisciplinary collaboration are required to establish a sustainable anthropocentric human-robot interactive ecosystem. • Concepts and aspects of optimal design for human-robot collaborative systems. • Methods for safe physical interactions between human and robots in agriculture. • Analysis on physical ergonomics risk factors in agricultural operations. • Benefits of safety culture in human-robot interactive ecosystems. [ABSTRACT FROM AUTHOR]

Several decades of development in the fields of robotics and automation have resulted in human–robot interaction is commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators (HOs) have been employed in numerous robotics and automation tasks. The presence of HOs continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This article examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programed behavior, and a methodology developed to explore the application of learning techniques to this area further. This article is supported by an example case study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realized with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. Note to Practitioners—This article presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated, and used within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behavior to reduce the impact of human performance variation and improve human–machine interaction (HMI). This article also presents a simulation-based case study to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine-learning approach to identify the complex influence of several identified human factors on human performance. This knowledge can be used to adjust the robotic behavior to match the predicted performance of multiple different operators over different scenarios. This adaptability reduces performance disparity by reducing idle times and enabling leaner production through workpiece-in-progress reduction. Future work will focus on expanding the intelligent capabilities of the proposed system to deal with uncertainty and improve decision-making ability. [ABSTRACT FROM AUTHOR]

This article presents a case study on the participation of activity-centered ergonomics researchers in a collaborative robot development research project. This empirical contribution is grounded in and framed by theoretical aspects relating to models of cooperation in a workerrobot system and to activity-centered ergonomics proposals relating to design project management. We therefore discuss: (1) the model of cooperation which is actually reified in the technology. Given the current potentialities of the technology, this model deals more with coordination than with cooperation, as it does not support dynamic management of the interdependence between worker-robot tasks; (2) the contributions and limits of the dual project management approach set up in the research consortium and in a factory. We stress that participation of ergonomists proved to be a necessary condition for designing a technology that potentially "preserves" health and performance; however, this participation was in no way sufficient as it did not support the future socio-organisational use of the technology. We conclude by highlighting ongoing research issues that focus on the aforementioned point by investigating decision-making processes relating to transitions towards a "Factory of the future" and to their supports, from political and historical-cultural perspectives. [ABSTRACT FROM AUTHOR]

We present algorithms and results for a robotic manipulation system that was designed to be easily programable and adaptable to various tasks common to industrial setting, which is inspired by the Industrial Assembly Challenge at the 2018 World Robotics Summit in Tokyo. This challenge included assembly of standard, commercially available industrial parts into 2D and 3D assemblies. We demonstrate three tasks that can be classified into 'peg-in-hole' and 'hole-on-peg' tasks and identify two canonical algorithms: spiral-based search and tilting insertion. Both algorithms use hand-coded thresholds in the force and torque domains to detect critical points in the assembly. After briefly summarizing the state of the art in research, we describe the strategy and approach utilized by the tested system, how it's design bears on its performance, statistics on 20 experimental trials for each task, lessons learned during the development of the system, and open research challenges that still remain. [ABSTRACT FROM AUTHOR]

Online recognition of gestures is critical for intuitive human–robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. DG features are obtained by applying data dimensionality reduction to raw data from sensors (resampling with cubic interpolation and principal component analysis). Experimental tests were conducted using the UC2017 hand gesture dataset with samples from eight different subjects. The classification models show an accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10 DGs using artificial neural networks. These results compare equally or favorably with different commonly used classifiers. Long short-term memory deep networks achieved similar performance in online frame-by-frame classification using raw incomplete data, performing better in terms of accuracy than static models with specially crafted features, but worse in training and inference time. The recognized gestures are used to teleoperate a robot in a collaborative process that consists in preparing a breakfast meal. [ABSTRACT FROM AUTHOR]

We propose a novel way of solving the issue of classification of out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in the Generative Adversarial Network (GAN) framework. A generative model augments the data set in an online fashion with new samples and stochastic target vectors, while a discriminative model determines the class of the samples. The approach was evaluated on the UC2017 SG and UC2018 DualMyo data sets. The generative models' performance was measured with a distance metric between generated and real samples. The discriminative models were evaluated by their accuracy on trained and novel classes. In terms of sample generation quality, the GAN is significantly better than a random distribution (noise) in mean distance, for all classes. In the classification tests, the baseline neural network was not capable of identifying untrained gestures. When the proposed methodology was implemented, we found that there is a trade-off between the detection of trained and untrained gestures, with some trained samples being mistaken as novelty. Nevertheless, a novelty detection accuracy of 95.4% or 90.2% (depending on the data set) was achieved with just 5% loss of accuracy on trained classes. [ABSTRACT FROM AUTHOR]

Human–robot collision avoidance is a key in collaborative robotics and in the framework of Industry 4.0. It plays an important role for achieving safety criteria while having humans and machines working side-by-side in unstructured and time-varying environment. This study introduces the subject of manipulator's on-line collision avoidance into a real industrial application implementing typical sensors and a commonly used collaborative industrial manipulator, KUKA iiwa. In the proposed methodology, the human co-worker and the robot are represented by geometric primitives (capsules). The minimum distance and relative velocity between them is calculated, when human/obstacles are nearby the concept of hypothetical repulsion and attraction vectors is used. By coupling this concept with a mathematical representation of robot's kinematics, a task level control with collision avoidance capability is achieved. Consequently, the off-line generated nominal path of the industrial task is modified on-the-fly so the robot is able to avoid collision with the co-worker safely while being able to fulfill the industrial operation. To guarantee motion continuity when switching between different tasks, the notion of repulsion-vector-reshaping is introduced. Tests on an assembly robotic cell in automotive industry show that the robot moves smoothly and avoids collisions successfully by adjusting the off-line generated nominal paths. • On-line collision avoidance for collaborative robot manipulators. • Adjusting off-line generated paths. • Motion continuity when switching between different tasks. • Assembly robotic cell in automotive industry. [ABSTRACT FROM AUTHOR]

This paper presents the challenges met in the development of a new framework for multiaxis and multirobot control. The increasing demand for multirobot collaboration and robotic assistance, both in industry and service applications, has raised the level of interactions between robots and humans in a shared environment with real-time constraints. Some scientific and technological issues need to be unlocked to ensure safe human–robot interactions: to guarantee the response time during the robot perception–action process, to cope with dynamic interactions with the robot environment, to secure the collaborations between several machines and humans, and to improve the integration of the robots at home and in open zones of production lines. The specifications of a new hardware and software framework are set with respect to these observations. The framework will meet complex research and industrial issues for the future of multiaxis and multirobot control. Before introducing our approach, a brief survey of existing frameworks and robotic middleware is given. The foundations of our approach are based on the following requirements: the framework must be real time, transferable, maintainable, and multimanufacturer. The framework design has also to guarantee the robustness of the machine interactions in a dynamic and collaborative environment. To evaluate the feasibility of our design strategy and assess its performance, we have developed mechatronic devices with a high level of human–machine collaboration. This paper outlines two robotic applications which require multirobot real-time synchronization and are based on the proposed framework. A temporal analysis demonstrates its robustness for multiaxis and multirobot control. Note to Practitioners—This paper was motivated by the problem of proposing a transferable framework dedicated to real-time multiaxis and multirobot control. Most of the existing middleware solutions do not unify accesses to the axis level and to the robot control level with a common programming approach and with real-time capability. This is a key problem for achieving multirobot synchronization and reaction times compatible with dynamic collaboration. Our framework is based on open robotics and object-oriented programming and implements the industrial standards for motion control. Multirobots control experiments with a high number of synchronized axis and heterogeneous hardware demonstrate the efficiency of the approach. [ABSTRACT FROM AUTHOR]

Human–robot interaction (HRI) is an extensive and diverse research topic that has been gaining importance in last years. Different fields of study have used HRI approaches for solving complicated problems, where humans and robots interact in some way to obtain advantages from their collaboration. Many industrial areas benefit by applying HRI strategies in their applications, and agriculture is one of the most challenging of them. Currently, field crops can reach highly autonomous levels whereas speciality crops do not. In particular, crops such as fruits and vegetables are still harvested manually, and also some tasks such as pruning and thinning have long been considered to be too complex to automate completely. In addition, several countries face the problem of farm labour shortages. As a consequence, the production process is affected. In this context, we survey HRI approaches and ap-plications focused on improving the working conditions, agility, efficiency, safety, productivity and profitability of agricultural processes, in cases where manual labour cannot be replaced by but can be complemented with robots. Highlights • Nowadays, robotics as machinery for planting or harvesting is solved in different countries. • However, harvest of crops like fruits and vegetables is still manually handled. • Several countries face the problem of lack of human labour force. • We survey the HRI perspectives and applications focused on improving working conditions, agility, efficiency, safety. • The focus is put in cases where human labour force cannot be replaced but complemented with robots. [ABSTRACT FROM AUTHOR]

The paradigm for robot usage has changed in the last few years, from a scenario in which robots work isolated to a scenario where robots collaborate with human beings, exploiting and combining the best abilities of robots and humans. The development and acceptance of collaborative robots is highly dependent on reliable and intuitive human-robot interaction (HRI) in the factory floor. This paper proposes a gesture-based HRI framework in which a robot assists a human co-worker delivering tools and parts, and holding objects to/for an assembly operation. Wearable sensors, inertial measurement units (IMUs), are used to capture the human upper body gestures. Captured data are segmented in static and dynamic blocks recurring to an unsupervised sliding window approach. Static and dynamic data blocks feed an artificial neural network (ANN) for static, dynamic, and composed gesture classification. For the HRI interface, we propose a parameterization robotic task manager (PRTM), in which according to the system speech and visual feedback, the co-worker selects/validates robot options using gestures. Experiments in an assembly operation demonstrated the efficiency of the proposed solution. [ABSTRACT FROM AUTHOR]

As robots become more ubiquitous, and their capabilities extend, novice users will require intuitive instructional information related to their use. This is particularly important in the manufacturing sector, which is set to be transformed under Industry 4.0 by the deployment of collaborative robots in support of traditionally low-skilled, manual roles. In the first study of its kind, this paper reports how static graphical signage can improve performance and reduce anxiety in participants physically collaborating with a semi-autonomous robot. Three groups of 30 participants collaborated with a robot to perform a manufacturing-type process using graphical information that was relevant to the task, irrelevant, or absent. The results reveal that the group exposed to relevant signage was significantly more accurate in undertaking the task. Furthermore, their anxiety towards robots significantly decreased as a function of increasing accuracy. Finally, participants exposed to graphical signage showed positive emotional valence in response to successful trials. At a time when workers are concerned about the threat posed by robots to jobs, and with advances in technology requiring upskilling of the workforce, it is important to provide intuitive and supportive information to users. Whilst increasingly sophisticated technical solutions are being sought to improve communication and confidence in human-robot co-working, our findings demonstrate how simple signage can still be used as an effective tool to reduce user anxiety and increase task performance. [ABSTRACT FROM AUTHOR]

This paper presents the formation control of a fleet of three small quadcopters in a motion capture environment. The dynamic model of a single quadcopter is derived for model predictive control (MPC) and then constraints are explained and expressed in an adequate manner to be included in the cost function for the optimization problem to be solved by the C/GMRES method. Two control architectures, centralized and decentralized, were implemented in the ROS framework and tested on the CrazyFlie quadcopter. First performances are assessed for a static reference, the formation regulation problem, then for a dynamic reference, the formation tracking one. Finally, computational cost of the MPC controllers is evaluated. [ABSTRACT FROM AUTHOR]

• A tool is proposed to start risk assessment in cobotic integration. • Risk assessment starts with determining the limits of the collaborative application. • Variability factors are identified; they represent limits to consider. • They relate to interactions between the cobot(s), human(s) and equipment involved. • They also relate to space throughout the application's life cycle. Risk assessment in collaborative robotics is a challenge. This is one reason why safety is a barrier to the adoption of cobotics. To overcome this challenge, this paper proposes five classes of collaborative applications and variability factors to launch the first step of risk assessment: determination of the use, space and time limits of the application, as per ISO 10218-2:2011. The five classes are Direct alternating collaboration, Direct-assistance collaboration, Indirect sequential collaboration, Indirect parallel collaboration, and Occasional workspace sharing without collaboration. The variability factors to consider when designing a collaborative application relate to the interaction between the cobot(s), human(s) and equipment involved and their characteristics within a space throughout the application's life cycle. The factors are concrete examples that help fulfill the standard's requirements regarding those limits. Since robots are partly completed machinery, one must consider the collaborative application as a whole, and not only the human-robot interaction. [ABSTRACT FROM AUTHOR]

A new approach to the monocular simultaneous localization and mapping (SLAM) problem is presented in this work. Data obtained from additional bearing-only sensors deployed as wearable devices is fully fused into an Extended Kalman Filter (EKF). The wearable device is introduced in the context of a collaborative task within a human-robot interaction (HRI) paradigm, including the SLAM problem. Thus, based on the delayed inverse-depth feature initialization (DI-D) SLAM, data from the camera deployed on the human, capturing his/her field of view, is used to enhance the depth estimation of the robotic monocular sensor which maps and locates the device. The occurrence of overlapping between the views of both cameras is predicted through geometrical modelling, activating a pseudo-stereo methodology which allows to instantly measure the depth by stochastic triangulation of matched points found through SIFT/SURF. Experimental validation is provided through results from experiments, where real data is captured as synchronized sequences of video and other data (relative pose of secondary camera) and processed off-line. The sequences capture indoor trajectories representing the main challenges for a monocular SLAM approach, namely, singular trajectories and close turns with high angular velocities with respect to linear velocities. [ABSTRACT FROM AUTHOR]

Metaheuristics have a substantial history in fine-tuning machine learning algorithms. They gained tremendous popularity in many application domains. Robotics on the other hand is a wide research discipline that embraces artificial intelligence in a complex individually-thinking robot and distributed robots. Recently, metaheuristics made a significant impact on the application areas of collaborating robotics. This new trend of collaborating robotics, offers the possibility of enhanced task performance, high reliability, low unit complexity and decreased cost over traditional robotic systems. Collaborating robots however are more than just networks of independent agents; they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment. On the conceptual level, these bots can be empowered by the logics of metaheuristic algorithms which share the same functionalities and capabilities. This paper reviews the recent advances of metaheuristic algorithms on robotics applications. A taxonomy is provided as a reference for robotics designers. [ABSTRACT FROM AUTHOR]

Industry 4.0 is the concept used to summarize the ongoing fourth industrial revolution, which is profoundly changing the manufacturing systems and business models all over the world. Collaborative robotics is one of the most promising technologies of Industry 4.0. Human-robot interaction and human-robot collaboration will be crucial for enhancing the operator's work conditions and production performance. In this regard, this enabling technology opens new possibilities but also new challenges. There is no doubt that safety is of primary importance when humans and robots interact in industrial settings. Nevertheless, human factors and cognitive ergonomics (i.e. cognitive workload, usability, trust, acceptance, stress, frustration, perceived enjoyment) are crucial, even if they are often underestimated or ignored. Therefore, this work refers to cognitive ergonomics in the design of human-robot collaborative assembly systems. A set of design guidelines has been developed according to the analysis of the scientific literature. Their effectiveness has been evaluated through multiple experiments based on a laboratory case study where different participants interacted with a low-payload collaborative robotic system for the joint assembly of a manufacturing product. The main assumption to be tested is that it is possible to improve the operator's experience and efficiency by manipulating the system features and interaction patterns according to the proposed design guidelines. Results confirmed that participants improved their cognitive response to human-robot interaction as well as the assembly performance with the enhancement of workstation features and interaction conditions by implementing an increasing number of guidelines. [ABSTRACT FROM AUTHOR]

This paper details a design-led research project that developed a prototype soft anthropomorphic end-effector for use with a collaborative robot in the construction industry. As the construction industry changes to meet the demands of increasing populations and a lack of skilled trades workers, new approaches are needed. This design research explored a novel approach to construction by focusing on designing a hybrid end-effector that allows a collaborative robot to carry out multiple repetitive tasks that are usually done manually during construction to increase productivity throughout all construction stages. The design research conducts an iterative design process using soft robotic techniques to replicate human hand elements such as muscles and ligaments, to create a hybrid end-effector capable of using numerous tools with one end-effector. The research aimed to reduce the high cost of requiring multiple end-effectors and improve productivity. This end-effector was tested for grip strength and the ability to use both power and precision grasp functions to pick up and use multiple tools including a paintbrush, a paint roller, a screwdriver, and screw. • Explores novel solutions for a multi-functional soft robotic anthropomorphic end-effector for the construction industry. • Design-led research methodology that follows an iterative process to develop a low-fidelity prototype. • Demonstrates a successful proof of concept through physical testing for 'light' construction activities, such as painting. • One advantage of the prototype end-effector is that it is safe for human-machine interaction. [ABSTRACT FROM AUTHOR]

Many applications of physical human–robot interaction (pHRI) seek to minimize the impedance felt by the operator. For large-scale operations, such as industrial material handling, two architectures have been proposed. The first uses a rigid robotic manipulator with force sensing and an admittance controller, such as the class of Intelligent Assist Devices (IADs). The second utilizes an underactuated macro-mini manipulator system, such as the uMan Assist Device. Given an application with a large payload, which of these two systems can offer the lowest inertia interaction? This work analyzes the effective inertia experienced by the operator in each of the systems. It compares the conditions under which each provides the lightest manipulation, conditions that depend on the payload as well as the frequency of manipulation. The results are validated on a large overhead robotic system configured alternatively in each of the configurations. [ABSTRACT FROM AUTHOR]