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Collaborative robots – “cobots” – are intended for direct interaction with a human worker, handling a shared payload. They are a marked departure from autonomous industrial robots which must be isolated from people for safety reasons. Cobots are also distinct from teleoperators, in which a human operator controls a robot and payload remotely. Cobots interact with people by producing software‐defined “virtual surfaces” which constrain and guide the motion of the shared payload, but add little or no power. Ergonomic as well as productivity benefits result from combining the strength and computer‐interface of the cobot with the sensing and dexterity of the human worker. This paper explains cobots as one approach to an emerging class of materials handling equipment called Intelligent Assist Devices (IADs). We describe two cobots of this class presently in industrial testbed settings. Future applications of cobots’ virtual surfaces are tool guidance in image guided surgery, and haptic display in which the surfaces of a CAD model can be felt.

The Industry 4.0 initiative – with its ultimate objective of revolutionizing the supply-chain – putted more emphasis on smart and autonomous systems, creating new opportunities to add flexibility and agility to automatic manufacturing systems. These systems are designed to free people from monotonous and repetitive tasks, enabling them to concentrate in knowledge-based jobs. One of these repetitive functions is the order-picking task which consists of collecting parts from storage (warehouse) and distributing them among the ordering stations. An order-picking system can also pick finished parts from working stations to take them to the warehouse. The purpose of this paper is to present a simplified model of a robotic order-picking system, i.e. a mobile manipulator composed by an automated guided vehicle (AGV), a collaborative robot (cobot) and a robotic hand.

Details about its implementation are also presented. The AGV is needed to safely navigate inside the factory infrastructure, namely, between the warehouse and the working stations located in the shop-floor or elsewhere. For that purpose, an ActiveONE AGV, from Active Space Automation, was selected. The collaborative robot manipulator is used to move parts from/into the mobile platform (feeding the working stations and removing parts for the warehouse). A cobot from Kassow Robots was selected (model KR 810), kindly supplied by partner companies Roboplan (Portugal) and Kassow Robotics (Denmark). An Arduino MKR1000 board was also used to interconnect the user interface, the AGV and the collaborative robot. The graphical user interface was developed in C# using the Microsoft Visual Studio 2019 IDE, taking advantage of this experience in this type of language and programming environment.

The resulting prototype was fully demonstrated in the partner company warehouse (Active Space Automation) and constitutes a possible order-picking solution, which is ready to be integrated into advanced solutions for the factories of the future.

A solution to fully automate the order-picking task at an industrial shop-floor was presented and fully demonstrated. The objective was to design a system that could be easy to use, to adapt to different applications and that could be a basic infrastructure for advanced order-picking systems. The system proved to work very well, executing all the features required for an order-picking system working in an Industry 4.0 scenario where humans and machines must act as co-workers. Although all the system design objectives were accomplished, there are still opportunities to improve and add features to the presented solution. In terms of improvements, a different robotic hand will be used in the final setup, depending on the type of objects that are being required to move. The amount of equipment that is located on-board of the AGV can be significantly reduced, freeing space and lowering the weight that the AGV carries. For example, the controlling computer can be substituted by a single-board-computer without any advantage. Also, the cobot should be equipped with a wrist camera to identify objects and landmark. This would allow the cobot to fully identify the position and orientation of the objects to pick and drop. The wrist camera should also use bin-picking software to fully identify the shape of the objects to pick and also their relative position (if they are randomly located in a box, for example). These features are easy to add to the developed mobile manipulator, as there are a few vision systems in the market (some that integrate with the selected cobot) that can be easily integrated in the solution. Finally, this paper reports a development effort that neglected, for practical reasons, all issues related with certification, safety, training, etc. A future follow-up paper, reporting a practical use-case implementation, will properly address those practical and operational issues.

The purpose of this paper is to introduce a method for stiffness modeling, identification and updating of collaborative robots (cobots). This method operates in real-time and with high precision and can eliminate the modeling error between the actual stiffness model and the theoretical stiffness model.

To simultaneously ensure the computational efficiency and modeling accuracy of the stiffness model, this method introduces the finite element substructure method (FESM) into the virtual joint method (VJM). The stiffness model of the cobots is built by integrating several 6-degree of freedom virtual joints that represent the elastic deformation of the cobot modules, and the stiffness matrices of these modules can be identified and obtained by the FESM. A model-updating method is proposed to identify stiffness influence coefficients, which can eliminate the modeling error between the actual prototype model and the theoretical finite element model.

The average relative error and the cycle time of the proposed method are approximately 6.14% and 1.31 ms, respectively. Compared with other stiffness modeling methods, this method not only has high modeling accuracy in high dexterity poses but also in low dexterity poses.

A hybrid stiffness modeling method is introduced to integrate the modeling accuracy of the FESM into the VJM. Stiffness influence coefficients are proposed to eliminate the modeling error between the theoretical and actual stiffness models.

This paper aims to illustrate the growing role of robots in the electronics industries.

Following a short introduction, this paper discusses robotic applications and products in three sectors of the electronics industry: semiconductor processing, printed circuit manufacture and electronic product assembly. Finally, conclusions are drawn.

The major application in semiconductor manufacture is the handling of silicon wafers during both front- and back-end processes and products include cleanroom certified multi-axis robotic arms, some mounted on mobile platforms, and automated guided vehicles. Applications in printed circuit board production include component handling and insertion, soldering, inspection, testing and packing. These exploit Cartesian, SCARA and six-axis articulated robots and cobots play an important role where automated and manual processes operate in close proximity. Electronic product assembly applications include part handling, soldering, bonding and sealing, screw driving, test and inspection and packaging. Cobots offer the benefits of a small footprint which allows deployment in the often limited space and use in proximity to humans. As yet, robotic assembly of complex electronic products such as smartphones and computers has not been realised for technical reasons.

This study provides a detailed review of robotic products and applications in three key sectors of the electronics industries.

This paper aims to provide a European perspective on the collaborative robot business and to consider the factors governing future market development.

Following an introduction, this first describes the collaborative robots launched recently by European manufacturers and their applications. It then discusses major European research activities and finally considers the factors stimulating the market.

This article shows that collaborative robots are being commercialised by the major European robot manufacturers as well as by several smaller specialists. Although most have low payload capacities they are inexpensive and offer a number of operational benefits, making them well suited to a range of existing and emerging applications. Europe has a strong research base and several EU-funded programmes aim to stimulate collaborative robot development and use. Rapid market development is anticipated, driven in the main by applications in electronic product manufacture and assembly; new applications in the automotive industry; uses by small to medium-sized manufacturers; and companies seeking robots to support agile production methods.

This paper provides a timely review of the rapidly developing European collaborative robot industry.

Although disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee assignment is also lacking. The hazard related with the tasks performed on disassembly lines on workers can be reduced by the use of robots or collaborative robots (cobots) instead of workers. This situation causes an increase in costs. The purpose of the study is to propose a novel version of the problem and to solve this bi-objective (minimizing cost and minimizing hazard simultaneously) problem.

The epsilon constraint method was used to solve the bi-objective model. Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization methods for Enrichment Evaluation (PROMETHEE) methods were used to support the decision-maker. In addition, a new criterion called automation rate was proposed. The effects of factors were investigated with full factor experiment design.

The effects of all factors were found statistically significant on the solution time. The combined effect of the number of tasks and number of workers was also found to be statistically significant.

In this study, for the first time in the literature, a disassembly line balancing and employee assignment model was proposed in the presence of heterogeneous workers, robots and cobots to simultaneously minimize the hazard to the worker and cost.

This study aims to provide an insight into recent technological developments and applications that are driving growth and change in the automotive robotics sector.

Following a short introduction which provides a commercial perspective, this study discusses the role of collaborative robots and provides examples of applications. It then considers robotic three-dimensional (3D) printing and artificial intelligence (AI)-enabled robots. This is followed by a discussion of the impact of the burgeoning electric vehicle sector. Finally, brief conclusions are drawn.

This study shows that the automotive robotics sector is growing more rapidly than vehicle production volumes due to factors which include the move to vehicle electrification, the growing use of cobots, 3D printing, which is moving from specialist and low volume to high volume component production, and the advent of AI-enabled robots.

This study provides details of the factors driving the automotive robotics market by discussing a selection of emerging applications and developments.

This paper aims to provide details of the use of sensing skins by robots through reference to commercial products and recent research.

Following an introduction, this paper first summarises the commercial status of robotic sensing skins. It then provides examples of recent safety skin research and is followed by a discussion of processing schemes applied to multiple sensor skin systems including humanoid robots. Examples of research into soft, flexible skins follow and the paper concludes with a short discussion.

The commercialisation of sensing skins has been driven by safety applications in the emerging cobot sector, and a market is emerging for skins that can be retrofitted to conventional robots. Sensing skin research is widespread and covers a multitude of sensing principles, technologies, materials and signal processing schemes. This will yield skins which could impart advanced sensory capabilities to robots and potential future uses include agile manipulation, search and rescue, personal care and advanced robotic prosthetics.

This paper provides details of the current role of sensing skins in robots and an insight into recent research.

The aim of this study is to create a robust and simple collision avoidance approach based on quaternion algebra for vision-based pick and place applications in manufacturing industries, specifically for use with industrial robots and collaborative robots (cobots).

In this study, an approach based on quaternion algebra is developed to prevent any collision or breakdown during the movements of industrial robots or cobots in vision system included pick and place applications. The algorithm, integrated into the control system, checks for collisions before the robot moves its end effector to the target position during the process flow. In addition, a hand–eye calibration method is presented to easily calibrate the camera and define the geometric relationships between the camera and the robot coordinate systems.

This approach, specifically designed for vision-based robot/cobot applications, can be used by developers and robot integrator companies to significantly reduce application costs and the project timeline of the pick and place robotics system installation. Furthermore, the approach ensures a safe, robust and highly efficient application for robotics vision applications across all industries, making it an ideal solution for various industries.

The algorithm for this approach, which can be operated in a robot controller or a programmable logic controller, has been tested as real-time in vision-based robotics applications. It can be applied to both existing and new vision-based pick and place projects with industrial robots or collaborative robots with minimal effort, making it a cost-effective and efficient solution for various industries.