DESIGN AND SIMULATION OF A DUAL-ARM ROBOT FOR MANUFACTURING OPERATIONS IN THE RAILCAR INDUSTRY

Ilesanmi Daniyan, Khumbulani Mpofu, Felix Ale, and Moses Oyesola

References

  1. [1] J. Kr¨uger, G. Schreck, and D. Surdilovic, Dual arm robot forflexible and cooperative assembly, CIRP Annals – Manufac-turing Technology, 60(1), 2011, 5–8.
  2. [2] C. Smith, Y. Karayiannidis, L. Nalpantidis, et al., Dual armmanipulation – A survey, Robotics and Autonomous Systems,60(10), 2012, 1340–1353.
  3. [3] Chen-Gang, Li-Tong, Chu-Ming, J.-Q. Xuan, and S.-H. Xu,Review on kinematics calibration technology of serial robots,International Journal of Precision Engineering and Manufac-turing, 15(8), 2014, 1759–1774.
  4. [4] K. Alexopoulos, D. Mavrikios, and G. Chryssolouris, Ergo-Toolkit: An ergonomic analysis tool in a virtual manufacturingenvironment, International Journal of Computer IntegratedManufacturing, 26(5), 2012, 440–452.
  5. [5] P. Tsarouchi, S. Makris, G. Michalos, et al., ROS basedcoordination of human robot cooperative assembly tasks – Anindustrial case study, Procedia CIRP, 37, 2015, 254–259.
  6. [6] P. Tsarouchi, S. Makris, and G. Chryssolouris, Human–Robotinteraction review and challenges on task planning and pro-gramming, International Journal of Computer Integrated Man-ufacturing, 29(8), 2016, 916–931.
  7. [7] S. Makris, G. Michalos, A. Eytan, and G. Chryssolouris,Cooperating robots for reconfigurable assembly operations:Review and challenges, Procedia CIRP, 3, 2012, 346–351.
  8. [8] B. Gleeson, K. Maclean, A. Haddadi, E. Croft, andJ. Alcazar, Gestures for industry intuitive human-robot com-munication from human observation, 8th ACM/IEEE Inter-national Conf. on Human-Robot Interaction (HRI), Tokyo,Japan, 2013, 349–356. doi:10.1109/HRI.2013.6483609.
  9. [9] G. Michalos, S. Makris, P. Tsarouchi, T. Guasch, D. Kon-tovrakis, and G. Chryssolouris, Design considerations for safehuman–robot collaborative work-places, Procedia CIRP, 37,2015, 248–253.
  10. [10] J. Kr¨uger, V. Katschinski, D. Surdilovic, and G. Schreck,PISA: Next generation of flexible assembly systems – Frominitial ideas to industrial prototypes, Proc. of 41st InternationalSymposium on Robotics, Munich, Germany, 2010, 84–89.
  11. [11] I. Iglesiasa, M.A. Sebasti´ana, and J.E. Aresc, Overview ofthe state of robotic machining: Current situation and futurepotential, Procedia Engineering, 132, 2015, 911–917.
  12. [12] J. Br¨uning, B. Denkena, M.A. Dittrich, and H.-S. Park, Sim-ulation based planning of machining processes with industrialrobots, Procedia Manufacturing, 6, 2016, 17–24.
  13. [13] B. Denkena, B. Bergmann, and T. Lepper, Design and opti-mization of a machining robot, Procedia Manufacturing, 14,2017, 89–96.12
  14. [14] Z. Pan, J. Polden, N. Larkin, S. Van Duin, and J. Norrish,Recent progress on programming methods for industrial robots,Robotics and Computer Integrated Manufacturing, 28(2), 2012,87–94.
  15. [15] M.F. Zaeh and O. Roesch, Improvement of the machiningaccuracy of milling robots, Production Engineering – Researchand Development, 8(6), 2014, 737–744.
  16. [16] W. Lin and H. Luo, Robotic welding, Handbook of manufac-turing engineering and technology, (London: Springer-Verlag,2014), 1–36. doi:10.1007/978-1-4471-4976-7_106-1.
  17. [17] P. Kah, M. Shrestha, E. Hiltunen, and J. Martikainen, Roboticarc welding sensors and programming in industrial applica-tions, International Journal of Mechanical and Materials En-gineering, 10(13), 2015, 1–16.
  18. [18] J. Fleischer, V. Schulze, J. Burtscher, and S. Dosch, Robot-based guiding of extrusion profiles-increase of guiding accuracyby considering the temperature-dependent effects, ProcediaCIRP, 18, 2014, 21–26.
  19. [19] L. Wang, A. Mohammed, and M. Onori, Remote roboticassembly guided by 3D models linking to a real robot, CIRPAnnals – Manufacturing Technology, 63, 2014, 1–4.
  20. [20] A. Zhu and Y. Chen, A machine-learning-based algorithm fordetecting a moving object, International Journal of Roboticsand Automation, 31(5), 2015, 402–408.
  21. [21] Q. Han, S. Sun, and H. Lang, Leader-follower formationcontrol of multi-robots based on bearing-only observations,International Journal of Robotics and Automation, 34(2), 2019,120–129.
  22. [22] C. Pupaza, G. Constantin, and T. Negrila, Computer aidedengineering of industrial robots, Proceedings in ManufacturingSystems, 9(2), 2014, 87–92.
  23. [23] M. Bugday and M. Karali, Design and optimization of industrialrobot arm to minimize redundant weight, Engineering Scienceand Technology, an International Journal, 22, 2019, 346–352.
  24. [24] P. Tsarouchia, S. Makrisa, G. Michalosa, et al., Robotizedassembly process using dual arm robot, Procedia CIRP, 23,2014, 47–52.
  25. [25] S. Makris, P. Tsarouchi, A-S. Matthaiakis, et al., Dual armrobot in cooperation with humans for flexible assembly, CIRPAnnals – Manufacturing Technology, 66, 2017, 13–16.
  26. [26] M. Rodrigues, M. Kormann, C. Schuhler, and P. Tomek,Robot trajectory planning using OLP and structured light3D machine vision, in G. Bebis, et al. (eds.), Advances invisual computing. Lecture Notes in Computer Science, Springer,Berlin, Heidelberg, 2013, 8034, 244–253.
  27. [27] S.K. Ong, J.W.S. Chong, and A.Y.C. Nee, A novel augmentedreality based robot programming and path planning methodol-ogy, Robotics and Computer-Integrated Manufacturing, 26(3),2010, 240–249.
  28. [28] H. Fang, S.K. Ong, and A.Y.C. Nee, Robot path and end-effector orientation planning using augmented reality, ProcediaCIRP, 3, 2012, 191–196.
  29. [29] B. Denkena and T. Lepper, Enabling an industrial robot formetal cutting operations, Procedia CIRP, 35, 2015, 79–84.
  30. [30] N. Papakostas, K. Alexopoulos, and A. Kopanakis, Integratingdigital manufacturing and simulation tools in the assemblydesign process: A cooperating robots cell case, CIRP Journalof Manufacturing Science and Technology, 4(1), 2011, 96–100.
  31. [31] M. Hofener and T.A. Schuppstuhl, Method for increasingthe accuracy of on-workpiece machining with small industrialrobots for composite repair, Production Engineering – Researchand Development, 8, 2014, 701–709.
  32. [32] Y. Bu, W. Liao, W. Tian, J. Zhang, and L. Zhang, Stiff-ness analysis and optimization in robotic drilling application,Precision Engineering, 49, 2017, 388–400.
  33. [33] G.-C. Vosniakos and E. Matsas, Improving feasibility of roboticmilling through robot placement optimization, Robotics andComputer-Integrated Manufacturing, 26, 2010, 517–525.
  34. [34] E. Abele, K. Schutzer, and M. Pischan, Tool path adaptionbased on optical measurement data for milling with industrialrobot, Production Engineering: Research and Development, 6,2012, 459–465.
  35. [35] M. Slamani, S. Gauthier, and J.-F. Chatelain, A study ofthe combined effects of machining parameters on cutting forcecomponents during high speed robotic trimming of CFRPs,Measurement, 59, 2015, 268–283.
  36. [36] I.A. Daniyan, M.O. Oyesola, K. Mpofu, and S. Nwankwo,Application of the fourth industrial revolution for high volumeproduction in the rail car industry, Chapter 10 in A. Akdoganand A.S. Vanli (eds.), Mass production, (London: Intech Open,2019), 88703, 153–167.
  37. [37] R.S. Khurmi and J.K.A. Gupta, Textbook of machine design,(New Delhi: Eurasia Publishing House Ltd., 2005).
  38. [38] M. Klumpp, Automation and artificial intelligence in busi-ness logistics systems: Human reactions and collaborationrequirements, International Journal of Logistics Research andApplications, 21(3), 2018, 224–242.
  39. [39] B. Matebese, D. Withey, and M.K. Banda, Path planning withthe leapfrog method in the presence of obstacles, IEEE Int.Conf. Rob. Biom. (ROBIO), Qingdao, China, 2016, 613–618.
  40. [40] B. Matebese, D. Withey, and M. Banda, Optimal pathsfor a mobile manipulator using the leapfrog method, 2019SAUPEC/RobMech/PRASA Conf., Bloemfontein, SouthAfrica, 2019, 42–48. 978-1-7281-0369-3/19.
  41. [41] A. Vamsikrishna, A.D. Mahindrakar, and S. Tiwari, Numericaland experimental implementation of leapfrog algorithm foroptimal control of a mobile robot, Indian Control Conf. (ICC),2017, 123–128.
  42. [42] X. Yang, J. Chen, and S.X. Yang, Dynamic bioinspired neuralnetwork for multi-robot formation control in unknown envi-ronments, International Journal of Robotics and Automation,30(3), 2015, 256–266.
  43. [43] G. Rishwaraj, S.G. Ponnambalam,, and R.K. Chetty, Mul-tirobot formation control using a hybrid posture estimationstrategy, International Journal of Robotics and Automation,29(4), 2014, 256–266.
  44. [44] P.C. Chen, J. Wan, A.N. Poo, and S.S. Ge, Formation andzoning control of multi-robot systems, International Journalof Robotics and Automation, 26(1), 2011, 35–48.
  45. [45] U. Asif and J. Iqbal, Motion planning of a walking robotusing attitude guidance, International Journal of Robotics andAutomation, 27(1), 2012, 41–48.
  46. [46] C. Ma, F. Yu, and Z. Luo, Simulations and experimentalresearch on a novel soft-terrain hexapod robot, InternationalJournal of Robotics and Automation, 30(3), 2015, 247–255.
  47. [47] L. Ssebazza and Y.-J. Pan, DGPS-based localization andpath following approach for outdoor wheeled mobile robots,International Journal of Robotics and Automation, 30(1), 2015,13–25.

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