Cost-effective Robotic Arm Simulation and System Verification

Cost-effective Robotic Arm Simulation and System Verification

Автор: Apostolos Tsagaris, Charalampos Polychroniadis, Anastasios Tzotzis, Panagiotis Kyratsis

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 2 vol.16, 2024 года.

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In recent years, the utilization of virtual environments in industry 4.0 has witnessed significant growth, particularly in the design, implementation, and management of robotic systems. This paper addresses the need for enhanced control in robotic arms by presenting the design and implementation of a 5DoF robotic arm transformed into a digital platform through specialized software. The methods employed involve detailed direct and inverse kinematic modeling to replicate the physical arm in a digital environment. Our measurements indicate an impressive accuracy ranging from 97% to 100% in the movements of the digital model, closely mirroring its physical counterpart. This research not only contributes to the development of simulation systems but also holds promise for the broader adoption of digital twins. The paper discusses the background, outlines the methodology, highlights key findings, and concludes with the potential future impact of this work on the advancement of robotic systems and simulation technologies.

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Robot Simulation, Robotic Arm, Arduino, Design, DH Parameters, Transformation Matrix, System Verification

Короткий адрес: https://sciup.org/15019359

IDR: 15019359   |   DOI: 10.5815/ijisa.2024.02.01

Список литературы Cost-effective Robotic Arm Simulation and System Verification

  • Oliff, H.; Liu, Y.; Kumar, M.; Williams, M.; Ryan, M. Reinforcement Learning for Facilitating Human-Robot-Interaction in Manufacturing. J. Manuf. Syst. 2020, 56, 326–340, doi:10.1016/j.jmsy.2020.06.018.
  • Zhang, G.Q.; Spaak, A.; Martinez, C.; Lasko, D.T.; Zhang, B.; Fuhlbrigge, T.A. Robotic Additive Manufacturing Process Simulation-towards Design and Analysis with Building Parameter in Consideration. IEEE Int. Conf. Autom. Sci. Eng. 2016, 2016-November, 609–613, doi:10.1109/COASE.2016.7743457.
  • Pieskä, S.; Kaarela, J.; Mäkelä, J. Simulation and Programming Experiences of Collaborative Robots for Small-Scale Manufacturing. 2018 2nd Int. Symp. Small-Scale Intell. Manuf. Syst. SIMS 2018 2018, 2018-January, 1–4, doi:10.1109/SIMS.2018.8355303.
  • Carpin, S.; Lewis, M.; Wang, J.; Balakirsky, S.; Scrapper, C. USARSim: A Robot Simulator for Research and Education. In Proceedings of the Proceedings 2007 IEEE International Conference on Robotics and Automation; 2007; pp. 1400–1405.
  • Kucuk, S.; Bingul, Z. An Off-Line Robot Simulation Toolbox. Comput. Appl. Eng. Educ. 2010, 18, 41–52, doi:10.1002/cae.20236.
  • Phanden, R.K.; Sharma, P.; Dubey, A. A Review on Simulation in Digital Twin for Aerospace, Manufacturing and Robotics. Mater. Today Proc. 2020, 38, 174–178, doi:10.1016/j.matpr.2020.06.446.
  • Garg, G.; Kuts, V.; Anbarjafari, G. Digital Twin for Fanuc Robots: Industrial Robot Programming and Simulation Using Virtual Reality. Sustain. 2021, 13, 1–22, doi:10.3390/su131810336.
  • Togias, T.; Gkournelos, C.; Angelakis, P.; Michalos, G.; Makris, S. Virtual Reality Environment for Industrial Robot Control and Path Design. Procedia CIRP 2021, 100, 133–138, doi:10.1016/j.procir.2021.05.021.
  • Liu, A.; Liu, H.; Yao, B.; Xu, W.; Yang, M. Energy Consumption Modeling of Industrial Robot Based on Simulated Power Data and Parameter Identification. Adv. Mech. Eng. 2018, 10, 1–11, doi:10.1177/1687814018773852.
  • Mitsi, S.; Bouzakis, K.D.; Mansour, G.; Sagris, D.; Maliaris, G. Off-Line Programming of an Industrial Robot for Manufacturing. Int. J. Adv. Manuf. Technol. 2005, 26, 262–267, doi:10.1007/s00170-003-1728-5.
  • Bres, A.; Monsarrat, B.; Dubourg, L.; Birglen, L.; Perron, C.; Jahazi, M.; Baron, L. Simulation of Friction Stir Welding Using Industrial Robots. Ind. Rob. 2010, 37, 36–50, doi:10.1108/01439911011009948.
  • Xiao, W.; Huan, J.; Dong, S. A STEP-Compliant Industrial Robot Data Model for Robot Off-Line Programming Systems. Robot. Comput. Integr. Manuf. 2014, 30, 114–123, doi:10.1016/j.rcim.2013.09.007.
  • Raza, K.; Khan, T.A.; Abbas, N. Kinematic Analysis and Geometrical Improvement of an Industrial Robotic Arm. J. King Saud Univ. - Eng. Sci. 2018, 30, 218–223, doi:10.1016/j.jksues.2018.03.005.
  • Barakat, A.N.; Gouda, K.A.; Bozed, K.A. Kinematics Analysis and Simulation of a Robotic Arm Using MATLAB. 4th Int. Conf. Control Eng. Inf. Technol. CEIT 2016 2017, 16–18, doi:10.1109/CEIT.2016.7929032.
  • Malik, A.A.; Masood, T.; Bilberg, A. Virtual Reality in Manufacturing: Immersive and Collaborative Artificial-Reality in Design of Human-Robot Workspace. Int. J. Comput. Integr. Manuf. 2020, 33, 22–37, doi:10.1080/0951192X.2019.1690685.
  • Bochmann, L.; Bänziger, T.; Kunz, A.; Wegener, K. Human-Robot Collaboration in Decentralized Manufacturing Systems: An Approach for Simulation-Based Evaluation of Future Intelligent Production. Procedia CIRP 2017, 62, 624–629, doi:10.1016/j.procir.2016.06.021.
  • Ribeiro, F.M.; Pires, J.N.; Azar, A.S. Implementation of a Robot Control Architecture for Additive Manufacturing Applications. Ind. Robot Int. J. Robot. Res. Appl. 2019, 46, 73–82, doi:10.1108/IR-11-2018-0226.
  • Bilberg, A.; Malik, A.A. Digital Twin Driven Human–Robot Collaborative Assembly. CIRP Ann. 2019, 68, 499–502, doi:https://doi.org/10.1016/j.cirp.2019.04.011.
  • Pérez, L.; Rodríguez-Jiménez, S.; Rodríguez, N.; Usamentiaga, R.; García, D.F. Digital Twin and Virtual Reality Based Methodology for Multi-Robot Manufacturing Cell Commissioning. Appl. Sci. 2020, 10, doi:10.3390/app10103633.
  • Kousi, N.; Gkournelos, C.; Aivaliotis, S.; Giannoulis, C.; Michalos, G.; Makris, S. Digital Twin for Adaptation of Robots’ Behavior in Flexible Robotic Assembly Lines. Procedia Manuf. 2019, 28, 121–126, doi:https://doi.org/10.1016/j.promfg.2018.12.020.
  • Choi, S.H.; Park, K.-B.; Roh, D.H.; Lee, J.Y.; Mohammed, M.; Ghasemi, Y.; Jeong, H. An Integrated Mixed Reality System for Safety-Aware Human-Robot Collaboration Using Deep Learning and Digital Twin Generation. Robot. Comput. Integr. Manuf. 2022, 73, 102258, doi: https://doi.org/10.1016/j.rcim.2021.102258.
  • Douthwaite, J.A.; Lesage, B.; Gleirscher, M.; Calinescu, R.; Aitken, J.M.; Alexander, R.; Law, J. A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics. Front. Robot. AI 2021, 8, 1–17, doi:10.3389/frobt.2021.758099.
  • Stączek, P.; Pizoń, J.; Danilczuk, W.; Gola, A. A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment—A Case Study. Sensors 2021, 21, doi:10.3390/s21237830.
  • Li, S.; Wang, Z.; Pang, Z.; Gao, M.; Duan, Z. Design and Analysis of 6-DoFs Upper Limb Assistant Rehabilitation Robot. Machines 2022, 10, doi:10.3390/machines10111035.
  • Jiang, J.; You, J. Kinematic Modeling and Simulation of a New Robot for Wingbox Internal Fastening Application. Machines 2023, 1–15.
  • Mourtzis, D.; Doukas, M.; Bernidaki, D. Simulation in Manufacturing: Review and Challenges. Procedia CIRP 2014, 25, 213–229, doi:10.1016/j.procir.2014.10.032.
  • Tzotzis, A.; Garcia-Hernandez, C.; Huertas-Talon, J.-L.; Tzetzis, D.; Kyratsis, P. Engineering Applications Using CAD Based Application Programming Interface. In Proceedings of the MATEC Web of Conferences; 2017; Vol. 94, pp. 1–7.
  • Rohmer, E.; Singh, S.P.N.; Freese, M. V-REP: A Versatile and Scalable Robot Simulation Framework. IEEE Int. Conf. Intell. Robot. Syst. 2013, 1321–1326, doi:10.1109/IROS.2013.6696520.
  • Pitonakova, L.; Giuliani, M.; Pipe, A.; Winfield, A. Feature and Performance Comparison of the V-REP, Gazebo and ARGoS Robot Simulators. In Proceedings of the Towards Autonomous Robotic Systems; Giuliani, M., Assaf, T., Giannaccini, M.E., Eds.; Springer International Publishing: Cham, 2018; pp. 357–368.
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