Comparison of a single- and dual-arm collaborative robots used for precision assembly

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The characteristics of the object intended for assembly and the RobotStudio environment are presented. The tool path, assembly times, accelerations and speeds of the robot arms were analyzed, as well as cost estimation and energy consumption. A multi-criteria comparative evaluation of collaborative robots can allow for their accurate selection in industrial applications. The presented simulation tests and analyzes can be used at the initial design stage of robotic cell for the assembly of selected components of the PC motherboard. process robotization collaborative robot assembly Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 1. INTRODUCTION Robotization of technological processes is one of the aspects of the Industry 4.0. Progressive competition and a dynamically changing market result in the need for robotization of production processes, which translates into: increased efficiency, precision and quality of manufactured products [ 1 ]. Robotization of processes is of particular importance in many industries: automotive, furniture, metal processing, electronics and medicine [ 2 ]. Most applications of industrial robots concern tasks related to object manipulation (pick and place), as well as welding, gluing, palleting [ 3 , 4 ], painting and quality assurance [ 5 , 6 ]. All of these operations can be part of the assembly process. Statistical data from robot suppliers and robotics associations consolidated by the IFR Statistical Department indicate that the electronics industry has been the main recipient of industrial robots since 2020. In 2021, the number of installed robots increased by 24%, and in 2022 by 10%. A growing trend is the use of collaborative robots in electronics assembly processes. The market share of collaborative robots, which includes the ABB YuMi robots, increased by 31%. Since 2017, the demand for robots in the electronics industry has increased by an average of 5% per year. The overall long-term growth trend is expected to continue [ 7 ]. Statistics provide grounds for research on the practical use of collaborative robots, especially those used in electronic parts assembly processes. The implementation of robotization and the selection of its most advantageous solution is based on selection criteria such as costs and time of the manufacturing process. To assess which variant is the most advantageous (for the selected evaluation criterion), the station or robotic line is modeled and simulated in a virtual environment. In such an environment, manipulation [ 8 – 10 ], grinding [ 11 ], palletizing [ 12 ], painting, milling, welding [ 13 , 14 ], spraying [ 15 ], sealing [ 16 ] and 3D printing [ 17 ] operations can be simulated. The authors of these publications indicate the benefits of offline robot programming, mainly checking the program code without any interruptions in the ongoing process, assessing the path and movement parameters of the tool or gripper, and, above all, the ability to analyze the time and costs of the programmed solution. Borboni et al discussed the advantages of simulating the work of collaborative robots in precise and expensive manufacturing processes to best reflect the real process [ 18 ]. One of the popular collaborative robots is ABB's YuMi, with an ergonomic shape modeled on human arms. The robot has 14 axes, 7 on each arm. Robot is equipped with many safety elements, which makes it possible to eliminate expensive security measures such as barriers, curtains, fences and allows work in the immediate vicinity of people. Chemweno et al. developed dynamic safety zones to activate the robot to stop at a user-defined distance from an object [ 19 ]. The ABB YuMi collaborative robot allows for the effective implementation of assembly processes where other industrial robots require special equipment and advanced grippers. The accuracy and repeatability of positioning at the level of 0.02 mm [ 20 ] allows the robot to be used in precise and repeatable assembly processes [ 21 ]. Using the YuMi robot, it is possible to simulate the technological process using one or two arms, which is impossible in the case of standard industrial robots of one kinematic structure. Precision engineering is an important element of modern manufacturing processes affecting quality, efficiency and innovation [ 22 ]. Due to the relatively short period of collaborative robots in industry, their capabilities have not been sufficiently presented in publications [ 23 ]. There are few studies describing collaborative robots in practical applications [ 24 ]. Giberti et al described the general application of collaborative robots in industrial processes [ 25 ]. Other works mainly describe ways of cooperation between humans and robots, taking into account technological safety [ 26 ], artificial intelligence methods for optimizing tool paths [ 27 ], structure and mechanism of operation of collaborative robots [ 28 ], their costs in assembly processes [ 29 ]. This research presents the results of simulations of the operation of two variants of robots collaborating in the assembly process along with the analysis of time, acceleration, speed signals and energy consumption, which has not been previously described in the literature. There are several variants of collaborative robots available, and this work compares a single-arm and dual-arm robot based on selected key criteria. From this point of view, it is justified to conduct research and develop methodologies in designing robotic cells with collaborative robots. 2. MATERIAL AND METHODS 2.1. CHARACTERISTICS OF THE ASSEMBLED PARTS The computer motherboard is one of the most important parts of the computer, on which other components are mounted. Its task is to enable communication between individual components, control the flow of energy and data, and ensure the stability and reliability of the entire system. An example computer motherboard is presented in Fig. 1 . Assembling the motherboard is a key step in building a computer. The essence of this process is the proper connection and protection of the processor and RAM chips on the motherboard to ensure the proper functioning of the computer [ 30 ]. Computer motherboards have different types of connectors to connect many components. One of the most important connectors is the processor slot to which the processor is mounted (Fig. 2 ). Another important connector is the RAM memory slot, where RAM modules are installed, enabling fast data processing (Fig. 3 ). Precise assembly of the computer motherboard is possible due to the high accuracy and repeatability of modern industrial robots. The vision system associated with the ABB YuMi collaborative robot is used to locate components and perform precise and correct assembly. The maximum jaw spacing is larger than the overall dimensions of the motherboard components that are mounted. Due to the fact that this robot is equipped with force sensors in the grippers, the risk of crushing computer motherboard components is eliminated. The arrangement of elements for assembly is shown in Fig. 4 . 2.2. ROBOTIC STATION A comparative evaluation of two variants of collaborative robots for the assembly task was carried out in ABB's RobotStudio. The most important functions and capabilities of the RobotStudio environment used for the analysis are: - construction and 3D visualization of a robotic station allowing to observe how the station works and how the robot moves during the assembly process before implementation in the real environment, - offline programming of a collaborative robot means creating a program, as well as its editing, testing and optimization without the requirement of physically working with the robot, - communication between the robot, external and peripheral devices and sensors makes it possible to conduct a full simulation of the entire production station, and also enables subsequent implementation of ready-made programs to all devices, not only the robot, - a library of functions in which many ready-made solutions can be used to quickly create simulations, including smart components for dynamic conversion of station inputs and outputs, - simulation of the robot and objects of the entire station in order to test and optimize the robot's movement trajectory, but also additional results of the robot's work such as collisions, speeds, accelerations, energy consumption, and many others, - a realistic user interface for offline programming that significantly mirrors online programming, for example a virtual operator panel (FlexPendant). In these studies, two collaborative robots are compared in single- and dual-arm versions. Figure 5 shows the compared ABB YuMi IRB 14000 and ABB YuMi IRB 14050 collaborative robots with a lifting capacity of 0.5 kg per arm with a dedicated 0.2 kg gripper attached. Both robots are equipped with grippers with one servo module (Fig. 6 ). The grippers are integrated with vision and vacuum to increase the precision of the tasks performed. The station was equipped with a conveyor belt and a plastic container designed specifically for the robotic production line used to assemble parts on the computer motherboard using the ABB YuMi collaborative robot. The view of the station with one and two-arm robot is shown in Figs. 7 and 8 . Rys.7. View of the station with a single-arm robot The single-arm robot takes and presses RAM number 1 first, then RAM number 2 and finally the processor. The dual-arm robot arms perform RAM assembly at the same time. The left arm takes RAM No. 1 and the right arm No. 2. Then the left arm mounts the processor and the right one at the same time presses the memory in the sockets. The flowchart of tasks performed by robots is shown in Fig. 9 . The trajectory of arm movements is presented in Fig. 10 (single-arm robot) and 11 (dual-arm robot). 2.3. RAPID PROGRAM CODE The robot program was written taking into account the elimination of collisions between the arms in the dual-arm robot variant. For obvious reasons, this problem does not occur in the case of a single-arm robot. Accurate movement was used in places where parts were collected and placed and speed of 50 mm/s. The maximum movement speed for each path was 200 mm/s. The movement between the programmed points of the tool path was in straight lines. The RAPID (Robot Application Programming Interface Description) code used in these studies is a programming language related to the RobotStudio software, which is a programming environment for ABB industrial robots. RAPID code written in RobotStudio can be compiled and sent to a real robot to control its operation. Table 1 shows the most important instructions used in the program. Table 1 Explanation of functions in the RAPID code Function Explanation CONST robtarget Home Assigning coordinates and names for the point MoveL Home,v200,z0 Linear movement command to a point called "Home" with a speed of v200 mm/s and zone z0 WaitTime 1; Command to wait the robot for a specified time Reset/Set PickRam Setting the signal that controls the movement of the mounting frame in position 0/1 2.4. METHODS OF COMPARATIVE EVALUATION OF COLLABORATIVE ROBOTS A comparative evaluation of two variants of collaborative robots was performed in the Signal Analyzer add-in for RobotStudio. The evaluation criteria are the process time, the speed and acceleration of the robot gripper movement and energy consumption. Additionally, assuming, among others: the value of profit, man-hour, purchase or energy cost, and by calculating efficiency, it is possible to analyze the costs of implementing a single- and dual-arm robot in the production process of computer motherboards. However, the results are universal and can constitute suggestions and a basis for choosing a robot variant also for other assembly processes. 3. RESULTS AND DISCUSSION 3.1. TIME OF THE ASSEMBLY PROCESS In variant I, a single-arm YuMi robot was used to assemble the computer motherboard. The total simulation time is 20.6 seconds. During the simulation, the robot performs all necessary tasks related to motherboard assembly, such as picking up components and placing them on the board. The times of individual tasks performed by the robot are presented in Table 2 and in the chart in Fig. 12. Table 2. The time needed to complete individual assembly tasks by a single-arm robot Task RAM 1 RAM 2 Pressing the RAMs Processor Free movements Total Time [s] 2.1 2 3.6 4.2 8.7 20.6 The largest part of the time involves free movements within the workstation, mainly reaching the gripper to the place where the part is picked up. These movements account for 43% of the time of the entire assembly process. This indicates that the robot performed activities related to the manipulation of elements for 8.7 s. The next longest task was mounting the processor due to the longest distance the robot arm had to move. The operation of pressing the RAMs took 3.6 s, or about 17% of the time of the entire operation, and mounting the RAMs was 10% for each of them. In variant II, a dual-arm YuMi robot was used. The total simulation time is 13.6 s. Both robot arms are able to perform tasks simultaneously, which speeds up the motherboard assembly process. During the simulation, one robotic arm can pick up parts while the other places them on the board. All values related to time analysis are presented in Table 3 and charts in Fig. 13 and 14. In the time analysis in the variant II, the percentage of time of each task on both arms was considered and, additionally, as in variant I, the ratio of the robot's work to free movements. Table 3. The time needed to perform individual assembly tasks by each arm of a dual-arm robot Task RAM 1 RAM 2 Pressing the RAMs Processor Free movement Total Left arm Time [s] 2.6 - - 3.9 7.1 13.6 Right arm Time [s] - 2.4 2.1 - 9.1 13.6 The operating time of the left robot arm in variant II includes a large amount of free movements of over 50%. This arm places the first RAM in its appropriate slot, and then reaches for the processor and mounts it in the designated place. These activities take 2.6 s and 3.9 s respectively. For the right arm, the free movements are much higher and amounts to 68%. This arm is responsible for mounting the second RAM and for pressing RAM No. 1 and 2. Both of these tasks took a total of 4.5 seconds. Taking into account the entire operation performed by both robot arms in variant II, summing up the assembly times of each part, free movements took only 20% of the time of the entire process. 3.2. SPEED AND ACCELERATION SIGNALS The sequence of movements of both compared robots (single- and dual-arm) in relation to time is presented in Table 4. Table 4. Robot movement sequences The course of the actual TCP speeds of the gripper of a single-arm robot and two arms of a dual-arm robot is shown in Fig. 15. Due to the fact that the programmed speeds may differ from the real ones due to the kinematics of the robot or the length of the movement path and the inability to achieve the programmed speed on a given section of the path. ABB YuMi robots have no difficulty in achieving a maximum speed of 200 mm/s on programmed paths. It is not achieved only when pressing the RAM bone with the right arm of the robot, because the section of the path between individual points is too short and it is not possible to develop the programmed speed. The speed of the TCP of the gripper to the position of picking parts from the container and placing them in the computer motherboard is reduced to 50 mm/s to minimize the risk of collisions and possible displacements of moved assembly parts. Figure 16 shows the accelerations achieved by the gripper's TCP during the process. The graph of the acceleration of the robot's arms at the TCP point versus time suggests that the largest acceleration peaks for all arms occur between 8 and 12 s. For a single-arm robot, it is the movement it performs over the processor in preparation for lifting it, and for a dual-arm robot, it is the movement associated with moving the processor to the appropriate place. The upward trends in most cases are related to starts after stops in the parts pick-up and put-down zones. Downward trends appear as the gripper approaches the target points where direct assembly is performed. 3.3. ENERGY CONSUMPTION Industrial robots are an integral part of modern production lines, contributing to increased efficiency and precision of processes. However, their growing importance requires taking into account energy consumptions, because energy is one of the key factors influencing the costs and sustainable development of the industry. In this case, two stations were analyzed - with a single- and dual-arm YuMi robot. These results are presented in Table 5 and in the graph in Fig. 17. Table 5. Energy consumption Time [s] Energy consumption [J] Variant I – single-arm robot 20.6 205.8 Variant II – dual-arm robot 13.6 260 During one assembly cycle, a single-arm robot consumes 205.8 J of energy to power the robot in 20.6 s. Whereas a dual-arm robot consumes 260 J in 13.6 s. Based on these results, it can be concluded that variant I - robot single-arm is more energy-efficient. However, a two-arm robot performs the assembly operation faster and using 20% more energy, which could turn out to be a much more financially profitable option in the case of large-scale production. When comparing collaborative robots, it should be noted that the operating time is not proportional to the energy consumption. 3.4. COSTS In the comparative analysis of the costs of two variants of collaborative robots, two points of view are taken into account: the profit and loss account from the application of robots in assembly, as well as the unit cost of robotic assembly. In order to assess which robot would be more profitable during production, a cost estimate was prepared. The first expense that must be incurred when implementing an robotic station is the cost of purchasing the robot. This cost estimate does not include the purchase prices for the stand's equipment, as it would be the same in both variants. The second important aspect is the efficiency of robots, i.e. the number of products they can assemble per month. The robots are located next to the assembly line and it was necessary to take into account the time during which the assembled frame with parts moves to the place where the process will start. The value of this allowance was set at 5 s and cycle times of 25.6 s for a single-arm robot and 18.6 s for a dual-arm robot. This cost estimate assumes that the robot will work in 3 shifts for 22 days a month (average value of working days). Based on these data, the results are presented in Table 6. The man-hour values were adopted on the basis of possibility of producing a given number of products per hour, cost of purchasing the robot, system maintenance and costs of an employee monitoring work. Monthly profit is calculated based on the equation (1): Monthly profit = (No. of cycles per month * Cost of producing 1 product) - (No. of shifts * No. of hours per shift * No. of working days per month * cost per man hour) - (Energy consumed in a month * cost per kWh) (1) It is important in cost analysis to determine the unit cost. Using division calculation, which is one of the basic ways of determining unit costs and calculated as the quotient of the sum of production costs and the production volume (2): where: K j – unit cost, K – total costs (purchase cost, energy, labor costs, etc.), P – production volume. Taking into account the data in Table 6, the unit cost for a single-arm robot is 0.047 €/pcs. and for dual-arm robot 0.058 €/pcs. Table 6. Cost and profit estimation of the robotic assembly (estimated data for 2023) [35, 36] Single-arm robot Dual-arm robot Unit Purchase cost 37369 [37] 61968 [38] € Cycle time 25.60 18.60 s Energy consumed per cycle 206 260 J Number of shifts 3 Number of hours per shift 8 Number of cycles per hour 141 194 pcs Energy consumed per hour 28941 50323 J Energy consumed per number of shifts 694575 1207742 J Number of working days/month 22 Energy consumed in a month 15280650 26570323 J Energy consumed in a month 4.24 7.38 kWh Number of cycles per month 74250 102194 pcs Energy consumption in the year 51 89 kWh Number of cycles per year 891000 1226323 pcs Cost per man hour 10.78 21.55 € Cost of production pcs. 0.32 € Cost per kWh 0.27 € Monthly profit 18312 21655 € Unit cost 0.047 0.058 €/pcs. For the analyzed assembly process, robots bring profit and their implementation would pay off after about 3 months. A two-arm robot is a more advantageous economic solution due to higher profits per month. Figure 18 shows a graph of the cumulative profit of a dual-arm robot versus a single-arm robot. 4. CONCLUSIONS Collaborative robots are becoming more and more widely used in assembly processes due to their precision of movements and the possibility of installation in the direct work zone of humans. Comparative research on collaborative robots is part of the current trend in the industry of using precision robots that can cooperate with humans and are additionally equipped with vision. Dual-arm robots can simultaneously perform various activities assigned to each of the arms separately. It is important to eliminate potential collisions that can occur not only between station equipment, but also between arms. Analyzing the work of collaborative robots in a virtual simulation environment has many benefits, including, most importantly, monitoring information about the movement trajectory, collisions, process time, energy consumption and movement speed. Offline programming capabilities facilitate the procedure of building robotic stations, as well as selecting robots and robot movement parameters without interruptions in the ongoing production process. During one assembly cycle of analyzed process, a single-arm robot consumes 205.8 J of energy in 20.6 s. Whereas a dual-arm robot consumes 260 J in 13.6 s. Based on these results, it can be concluded that a single-arm robot is more energy efficient. However, the two-arm robot performs the assembly operation faster and using 20% more energy, which could prove more profitable in the case of large-scale production. Analyzing the cost values, it can be concluded that both robots are profitable and their implementation would pay off after about 3 months. The obtained results are universal, and the simulation performed in a similar way allows for a comparison of collaborative robots in other technological processes. These studies can demonstrate a procedure for evaluating industrial robots in offline programming mode, suggesting a way in which other robotic manufacturing processes can be verified. Declarations AUTHOR CONTRIBUTIONS Katarzyna Peta: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Supervision, Verification. Marcin Wisniewski: Formal analysis, Writing - original draft, Visualization. Mikolaj Kotarski: Methodology, Formal analysis, Writing - original draft, Data curation, Visualization. Olaf Ciszak: Supervision, Methodology, Writing – original draft. DECLARATION OF COMPETING INTEREST The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ACKNOWLEDGMENTS The research was funded by the Polish Ministry of Science and Higher Education as a part of research subsidy, project number: 0614/SBAD/1579. 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Materials (Basel) 15:. https://doi.org/10.3390/ma15020611 Scott Mueller (2015) Upgrading and Repairing PCs https://www.gigabyte.com/ https://www.intel.pl/content/www/pl/pl/products/overview.html https://www.kingston.com/pl/memory/gaming/kingston-fury-beast-ddr4-rgb-memory https://new.abb.com/products/robotics/robots/collaborative-robots/yumi/irb-14000-yumi https://unchainedrobotics.de/en/products/robot/abb-yumi-irb-14000 https://unchainedrobotics.de/en/products/robot/single-arm-yumi-irb-14050 https://webshop.robotics.abb.com/us/catalog/product/view/id/63/ 2023. https://webshop.robotics.abb.com/us/catalog/product/view/id/84/s/dual-arm-yumi-irb-14000-assembly/ 2023. Supplementary Files GraphicalAbstract.png GRAPHICAL ABSTRACT Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4864365","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350539412,"identity":"458c8f78-b522-4d94-8709-cdc2b3155a77","order_by":0,"name":"Katarzyna Peta","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9915-838X","institution":"Poznan University of Technology: Politechnika Poznanska","correspondingAuthor":true,"prefix":"","firstName":"Katarzyna","middleName":"","lastName":"Peta","suffix":""},{"id":350539413,"identity":"fdcbaf67-c36d-4a3c-8627-4f95e393bb8c","order_by":1,"name":"Marcin Wiśniewski","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marcin","middleName":"","lastName":"Wiśniewski","suffix":""},{"id":350539414,"identity":"eef57231-9a1b-4ddb-8735-6c88ff78b055","order_by":2,"name":"Mikołaj Kotarski","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mikołaj","middleName":"","lastName":"Kotarski","suffix":""},{"id":350539415,"identity":"9dc8fcbe-02ca-4b0c-8147-38e117ca373b","order_by":3,"name":"Olaf Ciszak","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Olaf","middleName":"","lastName":"Ciszak","suffix":""}],"badges":[],"createdAt":"2024-08-05 21:49:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4864365/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4864365/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66767191,"identity":"4924ae8b-a443-4cee-9026-3ab9bcfba0ec","added_by":"auto","created_at":"2024-10-16 09:36:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":280872,"visible":true,"origin":"","legend":"\u003cp\u003eGigabyte computer motherboard [31]\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/d64645f6bfcc42d58b1e719e.png"},{"id":66767192,"identity":"93a46433-2b1c-469c-bbb3-145cb2f5482d","added_by":"auto","created_at":"2024-10-16 09:36:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112986,"visible":true,"origin":"","legend":"\u003cp\u003eIntel processor [32]\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/b7aa478dc2e5f4fa370b051f.png"},{"id":66767179,"identity":"6d1ef846-aa9c-4d28-8c16-038d91512b1d","added_by":"auto","created_at":"2024-10-16 09:36:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74046,"visible":true,"origin":"","legend":"\u003cp\u003eRAM module [33]\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/d614d2065452be1eab078e98.png"},{"id":66767175,"identity":"e79321c9-76b2-4c63-9411-8e201bedcf46","added_by":"auto","created_at":"2024-10-16 09:36:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209651,"visible":true,"origin":"","legend":"\u003cp\u003eAssembly parts\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/ed880d524ec5fcf4ec0da0f0.png"},{"id":66769132,"identity":"c774ad2a-c1d0-43eb-8cae-2718d833acec","added_by":"auto","created_at":"2024-10-16 09:52:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":178011,"visible":true,"origin":"","legend":"\u003cp\u003eCollaborative robots: single-arm ABB YuMi IRB 14050 and dual-arm ABB YuMi IRB 14000 [34]\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/2a5d17ef0f18c6e3f352872a.png"},{"id":66768577,"identity":"29d05c19-3544-40ce-815c-4cee8221775f","added_by":"auto","created_at":"2024-10-16 09:44:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84744,"visible":true,"origin":"","legend":"\u003cp\u003eThe gripper of the YuMi ABB collaborative robot [20]\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/aa1cd51bd90ca06e0a2ed748.png"},{"id":66768579,"identity":"08527708-df09-41ca-acb9-4156f873f9ec","added_by":"auto","created_at":"2024-10-16 09:44:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":215783,"visible":true,"origin":"","legend":"\u003cp\u003eView of the station with a single-arm robot\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/f65aaa2035d4b60f0d26a383.png"},{"id":66770205,"identity":"2f630f09-462f-4784-8fc9-c2af01e4b496","added_by":"auto","created_at":"2024-10-16 10:00:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":171676,"visible":true,"origin":"","legend":"\u003cp\u003eView of the station with a single-arm robot\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/d79f5ca6b642777ba8c709c6.png"},{"id":66770204,"identity":"72a57934-a1be-4d67-8ae7-cfb40769f0c9","added_by":"auto","created_at":"2024-10-16 10:00:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":39521,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of tasks performed by robots\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/2fa04c3136b331df831c48a9.png"},{"id":66767190,"identity":"9e836cfc-3ec8-4e0a-9c51-4905be996130","added_by":"auto","created_at":"2024-10-16 09:36:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":245300,"visible":true,"origin":"","legend":"\u003cp\u003eMotion trajectory for a single-arm robot\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/08e0981e0b03517f95d5f9b8.png"},{"id":66768585,"identity":"fa95c749-7031-4dad-a43f-4827baa47700","added_by":"auto","created_at":"2024-10-16 09:44:10","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":257805,"visible":true,"origin":"","legend":"\u003cp\u003eMotion trajectory for a dual-arm robot\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/6a98d0c525acc1a525f6974e.png"},{"id":66767193,"identity":"0182fe0b-e05f-464f-984a-0343ea0e8f95","added_by":"auto","created_at":"2024-10-16 09:36:11","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":61018,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of individual robot assembly tasks for variant I\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/8cad089c32c5eefe10a290ca.png"},{"id":66767182,"identity":"df03ec46-2c45-430c-aa1a-6ab4bc0a28f8","added_by":"auto","created_at":"2024-10-16 09:36:09","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":100016,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of individual assembly tasks for each robot arm for variant II\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/d19ed40081773b40c588d62a.png"},{"id":66768580,"identity":"2f2208b5-be7d-4ba1-9137-38b4a34831a8","added_by":"auto","created_at":"2024-10-16 09:44:09","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":35993,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of tasks for variant II - a dual-arm robot\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/5ee571577cb89a13e055f530.png"},{"id":66767185,"identity":"64b65488-ee40-4c62-ba16-b17a09aef564","added_by":"auto","created_at":"2024-10-16 09:36:09","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":94248,"visible":true,"origin":"","legend":"\u003cp\u003eTCP speeds versus time\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/126938d6caccb7e39cfe8496.png"},{"id":66769135,"identity":"6c59da5d-6b8e-4989-85d1-4b9d69334d70","added_by":"auto","created_at":"2024-10-16 09:52:09","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":64646,"visible":true,"origin":"","legend":"\u003cp\u003eTCP accelerations versus time\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/b37b0ec5da411beeb74fcf1a.png"},{"id":66769134,"identity":"e3fe5ebd-7028-4c44-a6ec-459cffded934","added_by":"auto","created_at":"2024-10-16 09:52:09","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":45114,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy consumption\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/a811ff03d3554c7510b91d33.png"},{"id":66768582,"identity":"802d4930-c7fa-4747-8715-90d750fc20af","added_by":"auto","created_at":"2024-10-16 09:44:09","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":43527,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative profit of a dual-arm robot versus a single-arm robot\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/1541e9200bf68027234ffd83.png"},{"id":104404361,"identity":"c2598357-9f76-4977-a2b8-45aed944c614","added_by":"auto","created_at":"2026-03-11 12:20:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2946055,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/daa7e165-4593-41de-ab09-557e5ac2ff37.pdf"},{"id":66768575,"identity":"41f3ae91-024f-4d4d-90df-3750a7a044c5","added_by":"auto","created_at":"2024-10-16 09:44:09","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":165213,"visible":true,"origin":"","legend":"\u003cp\u003eGRAPHICAL ABSTRACT\u003c/p\u003e","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-4864365/v1/ef17a857408b0d8135f02996.png"}],"financialInterests":"","formattedTitle":"Comparison of a single- and dual-arm collaborative robots used for precision assembly","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eRobotization of technological processes is one of the aspects of the Industry 4.0. Progressive competition and a dynamically changing market result in the need for robotization of production processes, which translates into: increased efficiency, precision and quality of manufactured products [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Robotization of processes is of particular importance in many industries: automotive, furniture, metal processing, electronics and medicine [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Most applications of industrial robots concern tasks related to object manipulation (pick and place), as well as welding, gluing, palleting [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], painting and quality assurance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. All of these operations can be part of the assembly process.\u003c/p\u003e \u003cp\u003eStatistical data from robot suppliers and robotics associations consolidated by the IFR Statistical Department indicate that the electronics industry has been the main recipient of industrial robots since 2020. In 2021, the number of installed robots increased by 24%, and in 2022 by 10%. A growing trend is the use of collaborative robots in electronics assembly processes. The market share of collaborative robots, which includes the ABB YuMi robots, increased by 31%. Since 2017, the demand for robots in the electronics industry has increased by an average of 5% per year. The overall long-term growth trend is expected to continue [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Statistics provide grounds for research on the practical use of collaborative robots, especially those used in electronic parts assembly processes.\u003c/p\u003e \u003cp\u003eThe implementation of robotization and the selection of its most advantageous solution is based on selection criteria such as costs and time of the manufacturing process. To assess which variant is the most advantageous (for the selected evaluation criterion), the station or robotic line is modeled and simulated in a virtual environment. In such an environment, manipulation [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], grinding [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], palletizing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], painting, milling, welding [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], spraying [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], sealing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and 3D printing [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] operations can be simulated. The authors of these publications indicate the benefits of offline robot programming, mainly checking the program code without any interruptions in the ongoing process, assessing the path and movement parameters of the tool or gripper, and, above all, the ability to analyze the time and costs of the programmed solution. Borboni et al discussed the advantages of simulating the work of collaborative robots in precise and expensive manufacturing processes to best reflect the real process [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the popular collaborative robots is ABB's YuMi, with an ergonomic shape modeled on human arms. The robot has 14 axes, 7 on each arm. Robot is equipped with many safety elements, which makes it possible to eliminate expensive security measures such as barriers, curtains, fences and allows work in the immediate vicinity of people. Chemweno et al. developed dynamic safety zones to activate the robot to stop at a user-defined distance from an object [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The ABB YuMi collaborative robot allows for the effective implementation of assembly processes where other industrial robots require special equipment and advanced grippers. The accuracy and repeatability of positioning at the level of 0.02 mm [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] allows the robot to be used in precise and repeatable assembly processes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Using the YuMi robot, it is possible to simulate the technological process using one or two arms, which is impossible in the case of standard industrial robots of one kinematic structure. Precision engineering is an important element of modern manufacturing processes affecting quality, efficiency and innovation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to the relatively short period of collaborative robots in industry, their capabilities have not been sufficiently presented in publications [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There are few studies describing collaborative robots in practical applications [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Giberti et al described the general application of collaborative robots in industrial processes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Other works mainly describe ways of cooperation between humans and robots, taking into account technological safety [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], artificial intelligence methods for optimizing tool paths [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], structure and mechanism of operation of collaborative robots [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], their costs in assembly processes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This research presents the results of simulations of the operation of two variants of robots collaborating in the assembly process along with the analysis of time, acceleration, speed signals and energy consumption, which has not been previously described in the literature. There are several variants of collaborative robots available, and this work compares a single-arm and dual-arm robot based on selected key criteria. From this point of view, it is justified to conduct research and develop methodologies in designing robotic cells with collaborative robots.\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.1. CHARACTERISTICS OF THE ASSEMBLED PARTS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe computer motherboard is one of the most important parts of the computer, on which other components are mounted. Its task is to enable communication between individual components, control the flow of energy and data, and ensure the stability and reliability of the entire system. An example computer motherboard is presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Assembling the motherboard is a key step in building a computer. The essence of this process is the proper connection and protection of the processor and RAM chips on the motherboard to ensure the proper functioning of the computer [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eComputer motherboards have different types of connectors to connect many components. One of the most important connectors is the processor slot to which the processor is mounted (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Another important connector is the RAM memory slot, where RAM modules are installed, enabling fast data processing (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003ePrecise assembly of the computer motherboard is possible due to the high accuracy and repeatability of modern industrial robots. The vision system associated with the ABB YuMi collaborative robot is used to locate components and perform precise and correct assembly. The maximum jaw spacing is larger than the overall dimensions of the motherboard components that are mounted. Due to the fact that this robot is equipped with force sensors in the grippers, the risk of crushing computer motherboard components is eliminated. The arrangement of elements for assembly is shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.2. ROBOTIC STATION\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA comparative evaluation of two variants of collaborative robots for the assembly task was carried out in ABB\u0026apos;s RobotStudio. The most important functions and capabilities of the RobotStudio environment used for the analysis are:\u003c/p\u003e\n \u003cp\u003e- construction and 3D visualization of a robotic station allowing to observe how the station works and how the robot moves during the assembly process before implementation in the real environment,\u003c/p\u003e\n \u003cp\u003e- offline programming of a collaborative robot means creating a program, as well as its editing, testing and optimization without the requirement of physically working with the robot,\u003c/p\u003e\n \u003cp\u003e- communication between the robot, external and peripheral devices and sensors makes it possible to conduct a full simulation of the entire production station, and also enables subsequent implementation of ready-made programs to all devices, not only the robot,\u003c/p\u003e\n \u003cp\u003e- a library of functions in which many ready-made solutions can be used to quickly create simulations, including smart components for dynamic conversion of station inputs and outputs,\u003c/p\u003e\n \u003cp\u003e- simulation of the robot and objects of the entire station in order to test and optimize the robot\u0026apos;s movement trajectory, but also additional results of the robot\u0026apos;s work such as collisions, speeds, accelerations, energy consumption, and many others,\u003c/p\u003e\n \u003cp\u003e- a realistic user interface for offline programming that significantly mirrors online programming, for example a virtual operator panel (FlexPendant).\u003c/p\u003e\n \u003cp\u003eIn these studies, two collaborative robots are compared in single- and dual-arm versions. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the compared ABB YuMi IRB 14000 and ABB YuMi IRB 14050 collaborative robots with a lifting capacity of 0.5 kg per arm with a dedicated 0.2 kg gripper attached.\u003c/p\u003e\n \u003cp\u003eBoth robots are equipped with grippers with one servo module (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The grippers are integrated with vision and vacuum to increase the precision of the tasks performed.\u003c/p\u003e\n \u003cp\u003eThe station was equipped with a conveyor belt and a plastic container designed specifically for the robotic production line used to assemble parts on the computer motherboard using the ABB YuMi collaborative robot. The view of the station with one and two-arm robot is shown in Figs. 7 and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eRys.7. View of the station with a single-arm robot\u003c/p\u003e\n \u003cp\u003eThe single-arm robot takes and presses RAM number 1 first, then RAM number 2 and finally the processor. The dual-arm robot arms perform RAM assembly at the same time. The left arm takes RAM No. 1 and the right arm No. 2. Then the left arm mounts the processor and the right one at the same time presses the memory in the sockets. The flowchart of tasks performed by robots is shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. The trajectory of arm movements is presented in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e (single-arm robot) and 11 (dual-arm robot).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.3. RAPID PROGRAM CODE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe robot program was written taking into account the elimination of collisions between the arms in the dual-arm robot variant. For obvious reasons, this problem does not occur in the case of a single-arm robot. Accurate movement was used in places where parts were collected and placed and speed of 50 mm/s. The maximum movement speed for each path was 200 mm/s. The movement between the programmed points of the tool path was in straight lines.\u003c/p\u003e\n \u003cp\u003eThe RAPID (Robot Application Programming Interface Description) code used in these studies is a programming language related to the RobotStudio software, which is a programming environment for ABB industrial robots. RAPID code written in RobotStudio can be compiled and sent to a real robot to control its operation. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the most important instructions used in the program.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eExplanation of functions in the RAPID code\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFunction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExplanation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONST robtarget Home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssigning coordinates and names for the point\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoveL Home,v200,z0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear movement command to a point called \u0026quot;Home\u0026quot; with a speed of v200 mm/s and zone z0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaitTime 1;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommand to wait the robot for a specified time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReset/Set PickRam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSetting the signal that controls the movement of the mounting frame in position 0/1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.4. METHODS OF COMPARATIVE EVALUATION OF COLLABORATIVE ROBOTS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA comparative evaluation of two variants of collaborative robots was performed in the Signal Analyzer add-in for RobotStudio. The evaluation criteria are the process time, the speed and acceleration of the robot gripper movement and energy consumption. Additionally, assuming, among others: the value of profit, man-hour, purchase or energy cost, and by calculating efficiency, it is possible to analyze the costs of implementing a single- and dual-arm robot in the production process of computer motherboards. However, the results are universal and can constitute suggestions and a basis for choosing a robot variant also for other assembly processes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003e3.1. TIME OF THE ASSEMBLY PROCESS\u003c/p\u003e\n\u003cp\u003eIn variant I, a single-arm YuMi robot was used to assemble the computer motherboard. The total simulation time is 20.6 seconds. During the simulation, the robot performs all necessary tasks related to motherboard assembly, such as picking up components and placing them on the board. The times of individual tasks performed by the robot are presented in Table 2 and in the chart in Fig. 12.\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;The time needed to complete individual assembly tasks by a single-arm robot\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003eRAM 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003eRAM 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003ePressing the RAMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eProcessor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFree movements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTime [s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7917%;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe largest part of the time involves free movements within the workstation, mainly reaching the gripper to the place where the part is picked up. These movements account for 43% of the time of the entire assembly process. This indicates that the robot performed activities related to the manipulation of elements for 8.7 s. The next longest task was mounting the processor due to the longest distance the robot arm had to move. The operation of pressing the RAMs took 3.6 s, or about 17% of the time of the entire operation, and mounting the RAMs was 10% for each of them.\u003c/p\u003e\n\u003cp\u003eIn variant II, a dual-arm YuMi robot was used. The total simulation time is 13.6 s. Both robot arms are able to perform tasks simultaneously, which speeds up the motherboard assembly process. During the simulation, one robotic arm can pick up parts while the other places them on the board. All values related to time analysis are presented in Table 3 and charts in Fig. 13 and 14. In the time analysis in the variant II, the percentage of time of each task on both arms was considered and, additionally, as in variant I, the ratio of the robot\u0026apos;s work to free movements.\u003c/p\u003e\n\u003cp\u003eTable 3. The time needed to perform individual assembly tasks by each arm of a dual-arm robot\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eTask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003eRAM 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003eRAM 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003ePressing the RAMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eProcessor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7917%;\"\u003e\n \u003cp\u003eFree movement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eLeft arm\u003c/p\u003e\n \u003cp\u003eTime [s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7917%;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eRight arm\u003c/p\u003e\n \u003cp\u003eTime [s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7917%;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe operating time of the left robot arm in variant II includes a large amount of free movements of over 50%. This arm places the first RAM in its appropriate slot, and then reaches for the processor and mounts it in the designated place. These activities take 2.6 s and 3.9 s respectively. For the right arm, the free movements are much higher and amounts to 68%. This arm is responsible for mounting the second RAM and for pressing RAM No. 1 and 2. Both of these tasks took a total of 4.5 seconds. Taking into account the entire operation performed by both robot arms in variant II, summing up the assembly times of each part, free movements took only 20% of the time of the entire process.\u003c/p\u003e\n\u003cp\u003e3.2. SPEED AND ACCELERATION SIGNALS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sequence of movements of both compared robots (single- and dual-arm) in relation to time is presented in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4. Robot movement sequences\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe course of the actual TCP speeds of the gripper of a single-arm robot and two arms of a dual-arm robot is shown in Fig. 15. Due to the fact that the programmed speeds may differ from the real ones due to the kinematics of the robot or the length of the movement path and the inability to achieve the programmed speed on a given section of the path.\u003c/p\u003e\n\u003cp\u003eABB YuMi robots have no difficulty in achieving a maximum speed of 200 mm/s on programmed paths. It is not achieved only when pressing the RAM bone with the right arm of the robot, because the section of the path between individual points is too short and it is not possible to develop the programmed speed. The speed of the TCP of the gripper to the position of picking parts from the container and placing them in the computer motherboard is reduced to 50 mm/s to minimize the risk of collisions and possible displacements of moved assembly parts.\u003c/p\u003e\n\u003cp\u003eFigure 16 shows the accelerations achieved by the gripper\u0026apos;s TCP during the process.\u003c/p\u003e\n\u003cp\u003eThe graph of the acceleration of the robot\u0026apos;s arms at the TCP point versus time suggests that the largest acceleration peaks for all arms occur between 8 and 12 s. For a single-arm robot, it is the movement it performs over the processor in preparation for lifting it, and for a dual-arm robot, it is the movement associated with moving the processor to the appropriate place. The upward trends in most cases are related to starts after stops in the parts pick-up and put-down zones. Downward trends appear as the gripper approaches the target points where direct assembly is performed.\u003c/p\u003e\n\u003cp\u003e3.3. ENERGY CONSUMPTION\u003c/p\u003e\n\u003cp\u003eIndustrial robots are an integral part of modern production lines, contributing to increased efficiency and precision of processes. However, their growing importance requires taking into account energy consumptions, because energy is one of the key factors influencing the costs and sustainable development of the industry. In this case, two stations were analyzed - with a single- and dual-arm YuMi robot. These results are presented in Table 5 and in the graph in Fig. 17.\u003c/p\u003e\n\u003cp\u003eTable 5. Energy consumption\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.4242%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1919%;\"\u003e\n \u003cp\u003eTime [s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.3838%;\"\u003e\n \u003cp\u003eEnergy consumption [J]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.4242%;\"\u003e\n \u003cp\u003eVariant I \u0026ndash; single-arm robot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1919%;\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.3838%;\"\u003e\n \u003cp\u003e205.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.4242%;\"\u003e\n \u003cp\u003eVariant II \u0026ndash; dual-arm robot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1919%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.3838%;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDuring one assembly cycle, a single-arm robot consumes 205.8 J of energy to power the robot in 20.6 s. Whereas a dual-arm robot consumes 260 J in 13.6 s. Based on these results, it can be concluded that variant I - robot single-arm is more energy-efficient. However, a two-arm robot performs the assembly operation faster and using 20% more energy, which could turn out to be a much more financially profitable option in the case of large-scale production. When comparing collaborative robots, it should be noted that the operating time is not proportional to the energy consumption.\u003c/p\u003e\n\u003cp\u003e3.4. COSTS\u003c/p\u003e\n\u003cp\u003eIn the comparative analysis of the costs of two variants of collaborative robots, two points of view are taken into account: the profit and loss account from the application of robots in assembly, as well as the unit cost of robotic assembly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to assess which robot would be more profitable during production, a cost estimate was prepared. The first expense that must be incurred when implementing an robotic station is the cost of purchasing the robot. This cost estimate does not include the purchase prices for the stand\u0026apos;s equipment, as it would be the same in both variants. The second important aspect is the efficiency of robots, i.e. the number of products they can assemble per month. The robots are located next to the assembly line and it was necessary to take into account the time during which the assembled frame with parts moves to the place where the process will start. The value of this allowance was set at 5 s and cycle times of 25.6 s for a single-arm robot and 18.6 s for a dual-arm robot. This cost estimate assumes that the robot will work in 3 shifts for 22 days a month (average value of working days). Based on these data, the results are presented in Table 6. The man-hour values were adopted on the basis of possibility of producing a given number of products per hour, cost of purchasing the robot, system maintenance and costs of an employee monitoring work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMonthly profit is calculated based on the equation (1):\u003c/p\u003e\n\u003cp\u003eMonthly profit\u0026nbsp;\u003cbr\u003e\u0026nbsp;= (No. of cycles per month * Cost of producing 1 product) \u0026nbsp;\u003cbr\u003e\u0026nbsp;- (No. of shifts * No. of hours per shift * No. of working days per month * cost per man hour)\u0026nbsp;\u003cbr\u003e- (Energy consumed in a month * cost per kWh) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (1)\u003c/p\u003e\n\u003cp\u003eIt is important in cost analysis to determine the unit cost. Using division calculation, which is one of the basic ways of determining unit costs and calculated as the quotient of the sum of production costs and the production volume (2):\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eK\u003csub\u003ej\u003c/sub\u003e\u003c/em\u003e \u0026ndash; unit cost,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eK\u003c/em\u003e \u0026ndash; total costs (purchase cost, energy, labor costs, etc.),\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026ndash; production volume.\u003c/p\u003e\n\u003cp\u003eTaking into account the data in Table 6, the unit cost for a single-arm robot is 0.047\u0026nbsp;\u0026euro;/pcs. and for dual-arm robot 0.058\u0026nbsp;\u0026euro;/pcs.\u003c/p\u003e\n\u003cp\u003eTable 6. Cost and profit estimation of the robotic assembly (estimated data for 2023)\u0026nbsp;[35, 36]\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle-arm robot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDual-arm robot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003ePurchase cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e37369\u0026nbsp;[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e61968\u0026nbsp;[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026euro;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eCycle time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e25.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003es\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eEnergy consumed per cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003eJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eNumber of shifts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44.898%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eNumber of hours per shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44.898%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eNumber of cycles per hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003epcs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eEnergy consumed per hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e28941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e50323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003eJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eEnergy consumed per number of shifts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e694575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e1207742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003eJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eNumber of working days/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44.898%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eEnergy consumed in a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e15280650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e26570323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003eJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eEnergy consumed in a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003ekWh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eNumber of cycles per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e74250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e102194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003epcs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eEnergy consumption in the year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003ekWh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eNumber of cycles per year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e891000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e1226323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003epcs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eCost per man hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e10.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e21.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026euro;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eCost of production pcs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44.898%;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026euro;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eCost per kWh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44.898%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026euro;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eMonthly profit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e18312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e21655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026euro;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.898%;\"\u003e\n \u003cp\u003eUnit cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4286%;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u0026euro;/pcs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the analyzed assembly process, robots bring profit and their implementation would pay off after about 3 months. A two-arm robot is a more advantageous economic solution due to higher profits per month. Figure 18 shows a graph of the cumulative profit of a dual-arm robot versus a single-arm robot.\u003c/p\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eCollaborative robots are becoming more and more widely used in assembly processes due to their precision of movements and the possibility of installation in the direct work zone of humans. Comparative research on collaborative robots is part of the current trend in the industry of using precision robots that can cooperate with humans and are additionally equipped with vision.\u003c/p\u003e \u003cp\u003eDual-arm robots can simultaneously perform various activities assigned to each of the arms separately. It is important to eliminate potential collisions that can occur not only between station equipment, but also between arms.\u003c/p\u003e \u003cp\u003eAnalyzing the work of collaborative robots in a virtual simulation environment has many benefits, including, most importantly, monitoring information about the movement trajectory, collisions, process time, energy consumption and movement speed. Offline programming capabilities facilitate the procedure of building robotic stations, as well as selecting robots and robot movement parameters without interruptions in the ongoing production process.\u003c/p\u003e \u003cp\u003eDuring one assembly cycle of analyzed process, a single-arm robot consumes 205.8 J of energy in 20.6 s. Whereas a dual-arm robot consumes 260 J in 13.6 s. Based on these results, it can be concluded that a single-arm robot is more energy efficient. However, the two-arm robot performs the assembly operation faster and using 20% more energy, which could prove more profitable in the case of large-scale production.\u003c/p\u003e \u003cp\u003eAnalyzing the cost values, it can be concluded that both robots are profitable and their implementation would pay off after about 3 months.\u003c/p\u003e \u003cp\u003eThe obtained results are universal, and the simulation performed in a similar way allows for a comparison of collaborative robots in other technological processes. These studies can demonstrate a procedure for evaluating industrial robots in offline programming mode, suggesting a way in which other robotic manufacturing processes can be verified.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAUTHOR CONTRIBUTIONS\u003c/p\u003e\n\u003cp\u003eKatarzyna Peta: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Supervision, Verification. Marcin Wisniewski: Formal analysis, Writing - original draft, Visualization. Mikolaj Kotarski: Methodology, Formal analysis, Writing - original draft, Data curation, Visualization. Olaf Ciszak: Supervision, Methodology, Writing – original draft.\u003c/p\u003e\n\u003cp\u003eDECLARATION OF COMPETING INTEREST\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGMENTS\u003c/p\u003e\n\u003cp\u003eThe research was funded by the Polish Ministry of Science and Higher Education as a part of research subsidy, project number: 0614/SBAD/1579.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSingh G, Banga VK (2022) Robots and its types for industrial applications. 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Materials (Basel) 15:. https://doi.org/10.3390/ma15020611\u003c/li\u003e\n \u003cli\u003eScott Mueller (2015) Upgrading and Repairing PCs\u003c/li\u003e\n \u003cli\u003ehttps://www.gigabyte.com/\u003c/li\u003e\n \u003cli\u003ehttps://www.intel.pl/content/www/pl/pl/products/overview.html\u003c/li\u003e\n \u003cli\u003ehttps://www.kingston.com/pl/memory/gaming/kingston-fury-beast-ddr4-rgb-memory\u003c/li\u003e\n \u003cli\u003ehttps://new.abb.com/products/robotics/robots/collaborative-robots/yumi/irb-14000-yumi\u003c/li\u003e\n \u003cli\u003ehttps://unchainedrobotics.de/en/products/robot/abb-yumi-irb-14000\u003c/li\u003e\n \u003cli\u003ehttps://unchainedrobotics.de/en/products/robot/single-arm-yumi-irb-14050\u003c/li\u003e\n \u003cli\u003ehttps://webshop.robotics.abb.com/us/catalog/product/view/id/63/ 2023.\u003c/li\u003e\n \u003cli\u003ehttps://webshop.robotics.abb.com/us/catalog/product/view/id/84/s/dual-arm-yumi-irb-14000-assembly/ 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"process robotization, collaborative robot, assembly","lastPublishedDoi":"10.21203/rs.3.rs-4864365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4864365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of these studies is to evaluate ABB YuMi collaborative robots in single- and dual-arm variants for precise assembly of a computer motherboard. The characteristics of the object intended for assembly and the RobotStudio environment are presented. The tool path, assembly times, accelerations and speeds of the robot arms were analyzed, as well as cost estimation and energy consumption. A multi-criteria comparative evaluation of collaborative robots can allow for their accurate selection in industrial applications. The presented simulation tests and analyzes can be used at the initial design stage of robotic cell for the assembly of selected components of the PC motherboard.\u003c/p\u003e","manuscriptTitle":"Comparison of a single- and dual-arm collaborative robots used for precision assembly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 09:36:02","doi":"10.21203/rs.3.rs-4864365/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e87cfcb-94a2-41ca-bad0-d1034819f1ae","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T05:15:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-16 09:36:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4864365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4864365","identity":"rs-4864365","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}