Features of software implementation and three-dimensional modelling of the automated certification of industrial robot metrics in coppeliasim environment

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Oleksandr Dobrzhanskyi
Anton Kravchuk
Eugene Puhovsky
Volodymyr Savkiv

Abstract

The paper is devoted to the problem of automated certification of industrial robot metrics (IRM) in the CoppeliaSim software environment. The authors in detail consider the capabilities and tools of the CoppeliaSim software environment for measuring and evaluating such spatial parameters of the IR as the working area and the configuration of the toolʼs geometric position. The study of mechanical, technical and technological systems and virtual environments has long been the subject of scientific activity. For example, the papers describe mathematical models of the behaviour of flexible mechanical transmissions, the research of which is carried out without taking into account automated implementation. There are also known studies by the method of computer modelling of technical systems for the power load of cutting tools using highly specialized software environments. Moreover, the features of the software implementation of the proposed models are usually not considered. The material proposed in this paper focuses on universal software products and programming languages for three- dimensional modelling. The results of the investigation of methods and approaches to the automated implementation of the metric, which make it possible to ensure reliability and accuracy in the further synthesis of elements of robotic technologies, such as optimisation of equipment placement, formation of the optimal trajectory of movement of links of the manipulation system of the IR with a tool or a gripper are presented in this paper. Rationale for the use of spatial 3D modelling with full-size virtual models of the IR in the CoppeliaSim environment are presented in the paper. The authors analyse the tools and instruments that allow taking into account the influence of primarily spatial factors on the metric of the IR, such as geometric parameters of the IR design, tools, gripper, possible limitations due to the design and technological features of the technological equipment. This paper can be useful for researchers, engineers and students studying IRs in terms of their automated modelling and analysis

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References

1. Hlembotska L., Balytska N., Melnychuk P., Melnyk O. (2019) Computer modelling power load of face mills with cylindrical rake face of inserts in machining difficult-to-cut materials. Scientific Journal of TNTU, vol. 93, no. 1, pp. 70–80. Doi: 10.33108/visnyk_tntu2019.01.070.

2. Lutsiv I., Dubyniak T., Manziy O., Andreichuk S. (2022) Mathematical represantation of the branch kinematics of a transmission with descrete flexible connection. Scientific Journal of TNTU, vol. 106, no. 2, pp. 5–15. Doi:10.33108/visnyk_tntu2022.02.005.

3. Liu Y., Zhao L., Liang M., Wang F. (2024) Kinematics Study of Six-Axis Industrial Robots Based on Virtual Simulation Technology. Proceedings of the 13th International Conference on Computer Engineering and Networks. CENet 2023. Lecture Notes in Electrical Engineering, Springer, vol. 1125, pp. 520–531. Available at: https://www.researchgate.net/publication/377133228_Kinematics_Study_of_Six-Axis_Industrial_Robots_ Based_on_Virtual_Simulation_Technology. https://doi.org/10.1007/978-981-99-9239-3_51

4. Li L., Neau M., Ung T., Buche C. (2024) Crossing Real and Virtual: Pepper Robot as an Interactive Digital Twin. RoboCup 2023: Robot World Cup XXVI. RoboCup 2023. Lecture Notes in Computer Science, Springer, vol. 14140, pp. 275–286. https://doi.org/10.1007/978-3-031-55015-7_23

5. Guanopatin A. V., Ortiz J. S. (2023) Meaningful Learning Processes of Service Robots Through Virtual Environments. Proceedings of the Future Technologies Conference (FTC), Vol. 1. FTC 2023. Lecture Notes in Networks and Systems, Springer, vol. 813, pp. 59–73. https://doi.org/10.1007/978-3-031-47454-5_5

6. Ge Y., Hu Y., Sun X. (2023) Co-Design of Service Robot Applications Using Virtual Reality. Human Factors in Virtual Environments and Game Design. AHFE. International Conference. AHFE Open Access, AHFE International, USA, vol. 96, pp. 65–73. https://doi.org/10.54941/ahfe1003868

7. Liu R., Wandeto J., Nageotte F., Zanne P., de Mathelin M., Dresp-Langley B. (2023) Spatiotemporal Modeling of Grip Forces Captures Proficiency in Manual Robot Control. Bioengineering, vol. 10, 59, pp. 1–18. Available at: https://www.researchgate.net/publication/369021403_Spatiotemporal_modeling_of_grip_forces_captures_proficiency_in_manual_robot_control https://doi.org/10.3390/bioengineering10010059

8. Junya Y., Kenji T., Takahiro W. (2024) Effect of Presenting Stiffness of Robot Hand to Human on Human-Robot Handovers. TechRxiv, April 01, pp. 1–8. https://doi.org/10.36227/techrxiv.171198254.46018996/v1

9. Lu Y., Deng B., Wang Z., Zhi P., Li Y., Wang S. (2022) Hybrid Physical Metric For 6-DoF Grasp Pose Detection, arXiv.2206.11141, vol. 1, pp. 1–7. Available at: https://www.researchgate.net/publication/361479841_Hybrid_Physical_Metric_For_6-DoF_Grasp_Pose_Detection. https://doi.org/10.1109/ICRA46639.2022.9811961

10. Barnfather J., Goodfellow M.J., Abram T. (2016) A performance evaluation methodology for robotic machine tools used in large volume manufacturing. Robotics and Computer-Integrated Manufacturing, vol. 37, pp. 49–56. Available at: https://www.researchgate.net/publication/281716706_A_performance_evaluation_methodology_for_robotic_machine_tools_used_in_large_volume_manufacturing https://doi.org/10.1016/j.rcim.2015.06.002

11. Slamani M., Nubiola A., Bonev I. (2012) Assessment of the positioning performance of an industrial robot. Industrial Robot: An International Journal, vol. 39, no. 1 , pp. 57–68. Available at: https://www.researchgate.net/publication/238308032_Assessment_of_the_positioning_performance_of_an_industrial_robot https://doi.org/10.1108/01439911211192501

12. Panneerselvam S., Karthikeyan R. (2020) Simulation of Robot Kinematic Motions using Collision Mapping Planner using Robo Dk Solver. International Journal of Innovative Technology and Exploring Engineering, vol. 9, pp. 2278–3075. Available at: https://www.researchgate.net/publication/348232778_Simulation_of_Robot_Kinematic_Motions_using_Collision_Mapping_Planner_using_Robo_Dk_Solver. https://doi.org/10.35940/ijitee.J7588.0991120

13. Chakraborty S., Aithal S. (2021) ABB IRB 120-30.6 Build Procedure in RoboDK. International Journal of Management, Technology, and Social Sciences, vol. 6, no. 2, pp. 256–264. Available at: https://www.researchgate.net/publication/357158252_ABB_IRB_120-306_Build_Procedure_in_Robo DK. https://doi.org/10.47992/IJMTS.2581.6012.0169

14. Henriques J., Neto E., Paiva J., Carneiro S., Letícia L., Alexandre F., Flávio C., Giuliano. Trajectory Generation Using RoboDK for a Staubli SCARA TS 60 Robot. 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA), Grimstad, Norway. 2023, pp. 121–126. https://doi.org/10.1109/ICCMA59762.2023.10374649

15. Goryl K., Pollák M. (2023) Calibration of Panasonic TM-2000 Welding Robot Using Simulation Software. EAI ARTEP 2023. EAI International Conference on Automation and Control in Theory and Practice. Springer Innovations in Communication and Computing. Springer, pp. 273–284. https://doi.org/10.1007/978-3-031-31967-9_21

16. Salihovic I., Skamo A., Jokic D. (2021). RoboDK to MATLAB Joint Position Transformation. 2021 Selected Issues of Electrical Engineering and Electronics (WZEE), Rzeszow, Poland, pp. 1–6. https://doi.org/10.1109/WZEE54157.2021.9576924

17. Chakraborty S., Aithal S. (2021) Forward and Inverse Kinematics Demonstration using RoboDK and C#. International Journal of Applied Engineering and Management Letters (IJAEML), vol. 5, no. 1, pp. 97–105. https://doi.org/10.47992/IJAEML.2581.7000.0095

18. Kyrylovych V., Kravchuk A., Melnychuk P., Mohelnytska L. (2021). Automated Attestation of Metrics for Industrial Robots’ Manipulation Systems. Advanced Manufacturing Processes II. InterPartner 2020. Lecture Notes in Mechanical Engineering. Springer, pp. 813–822. https://doi.org/10.1007/978-3-030-68014-5_79

19. Kyrylovych V., Kravchuk A. (2023) A Three-Tiered Approach to The Initial Stages of Design of Collaborative Robotic. Technical sciences. Technologies Herald of Khmelnytskyi national university, issue 4, no. 323, pp. 180–187. Doi:10.31891/2307-5732-2023-323-4-180-187 81.5.

20. CoppeliaSim Homepage. Available at: http://www.coppeliarobotics.com.

21. Chakraborty S., Aithal S. (2021) An Inverse Kinematics Demonstration of a Custom Robot using C# and CoppeliaSim. International Journal of Case Studies in Business, IT, and Education (IJCSBE), vol. 5, no. 1, pp. 78–87. https://doi.org/10.47992/IJCSBE.2581.6942.0102

22. ABB Homepage. Available at: https://new.abb.com.

23. OnRobot – RG2 gripper. Available at: https://onrobot.com/en/products/rg2-gripper.

24. Escobar L., Kaveh K. (2020) Convex polytopes, algebraic geometry, and combinatorics, Notices of the American Mathematical Society, vol. 67, no. 8, pp. 1116–1123. https://doi.org/10.1090/noti2137

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