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Kinematic Optimization of a Robotic Arm for Automation Tasks with Human Demonstration (2401.16801v1)

Published 30 Jan 2024 in cs.RO

Abstract: Robotic arms are highly common in various automation processes such as manufacturing lines. However, these highly capable robots are usually degraded to simple repetitive tasks such as pick-and-place. On the other hand, designing an optimal robot for one specific task consumes large resources of engineering time and costs. In this paper, we propose a novel concept for optimizing the fitness of a robotic arm to perform a specific task based on human demonstration. Fitness of a robot arm is a measure of its ability to follow recorded human arm and hand paths. The optimization is conducted using a modified variant of the Particle Swarm Optimization for the robot design problem. In the proposed approach, we generate an optimal robot design along with the required path to complete the task. The approach could reduce the time-to-market of robotic arms and enable the standardization of modular robotic parts. Novice users could easily apply a minimal robot arm to various tasks. Two test cases of common manufacturing tasks are presented yielding optimal designs and reduced computational effort by up to 92%.

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References (34)
  1. H. M. Do, C. Park, and J. H. Kyung, “Dual arm robot for packaging and assembling of it products,” in IEEE International Conference on Automation Science and Engineering, 2012, pp. 1067–1070.
  2. M. Beschi, S. Mutti, G. Nicola, M. Faroni, P. Magnoni, E. Villagrossi, and N. Pedrocchi, “Optimal robot motion planning of redundant robots in machining and additive manufacturing applications,” Electronics, vol. 8, p. 1437, Dec 2019.
  3. P. Chutima, “A comprehensive review of robotic assembly line balancing problem,” J. of Intelligent Manuf., vol. 8, pp. 1572–8145, 2020.
  4. M. Cefalo, G. Oriolo, and M. Vendittelli, “Planning safe cyclic motions under repetitive task constraints,” in IEEE International Conference on Robotics and Automation, 2013, pp. 3807–3812.
  5. V. Bloch, A. Degani, and A. Bechar, “A methodology of orchard architecture design for an optimal harvesting robot,” Biosystems Engineering, vol. 166, pp. 126–137, 2018.
  6. M. Perrollaz, S. Khorbotly, A. Cool, J. Yoder, and E. Baumgartner, “Teachless teach-repeat: Toward vision-based programming of industrial robots,” in IEEE Inter. Conf. on Rob. & Auto., 2012, pp. 409–414.
  7. S. Schaal, “Learning from demonstration,” in Advances in Neural Information Processing Systems, M. Mozer, M. Jordan, and T. Petsche, Eds., vol. 9.   MIT Press, 1996.
  8. S. Patel and T. Sobh, “Task based synthesis of serial manipulators,” Journal of Advanced Research, vol. 6, no. 3, pp. 479 – 492, 2015.
  9. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948.
  10. Shicai Shi, Xiaohui Gao, Zongwu Xie, Fenglei Ni, Hong Liu, E. Kraemer, G. Hirzinger, and S. C. Shi, “Development of reconfigurable space robot arm,” in International Symposium on Systems and Control in Aerospace and Astronautics, 2006, pp. 6 pp.–143.
  11. R. Vijaykumar, K. Waldron, and M. Tsai, “Geometric optimization of serial chain manipulator structures for working volume and dexterity,” Int. Journal of Robotics Research, vol. 5, no. 2, pp. 91–103, 1986.
  12. M. Stock and K. Miller, “Optimal kinematic design of spatial parallel manipulators: Application to linear delta robot,” Journal of Mechanical Design, vol. 125, 06 2003.
  13. A. Zeiaee, R. Soltani-Zarrin, R. Langari, and R. Tafreshi, “Kinematic design optimization of an eight degree-of-freedom upper-limb exoskeleton,” Robotica, vol. 37, no. 12, p. 2073–2086, 2019.
  14. W. S. You, Y. Lee, G. Kang, H. Oh, J. Seo, and H. Choi, “Kinematic design optimization for anthropomorphic robot hand based on interactivity of fingers,” Intelligent Service Robotics, pp. 1–12, 04 2019.
  15. M. J. H. Lum, J. Rosen, M. N. Sinanan, and Hannaford B, “Kinematic optimization of a spherical mechanism for a minimally invasive surgical robot,” in IEEE Int. Conf. on Rob. & Aut., 2004, pp. 829–834.
  16. A. Kuntz, C. Bowen, C. Baykal, A. W. Mahoney, P. L. Anderson, F. Maldonado, R. J. Webster, and R. Alterovitz, “Kinematic design optimization of a parallel surgical robot to maximize anatomical visibility via motion planning,” in IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 926–933.
  17. J. Rastegar and B. Fardanesh, “Manipulation workspace analysis using the monte carlo method,” Mechanism and Machine Theory, vol. 25, no. 2, pp. 233 – 239, 1990.
  18. N. Vahrenkamp, T. Asfour, G. Metta, G. Sandini, and R. Dillmann, “Manipulability analysis,” in IEEE-RAS International Conference on Humanoid Robots, 2012, pp. 568–573.
  19. L. Stocco, S. E. Salcudean, and F. Sassani, “Fast constrained global minimax optimization of robot parameters,” Robotica, vol. 16, no. 6, p. 595–605, 1998.
  20. C. Giladi and A. Sintov, “Manifold learning for efficient gravitational search algorithm,” Information Sciences, vol. 517, pp. 18 – 36, 2020.
  21. S. Khatami and F. Sassani, “Isotropic design optimization of robotic manipulators using a genetic algorithm method,” in IEEE Internatinal Symposium on Intelligent Control, 2002, pp. 562–567.
  22. J. Bryson, X. Jin, and S. Agrawal, “Optimal design of cable-driven manipulators using particle swarm optimization,” ASME. J. Mechanisms Robotics, vol. 8, no. 4, 2016.
  23. Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, and N. Heess, “Reinforcement and imitation learning for diverse visuomotor skills,” in Robotics: Science and Systems, 2018.
  24. K. Ayusawa and E. Yoshida, “Motion retargeting for humanoid robots based on simultaneous morphing parameter identification and motion optimization,” IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1343–1357, 2017.
  25. L. Smith, N. Dhawan, M. Zhang, P. Abbeel, and S. Levine, “Avid: Learning multi-stage tasks via pixel-level translation of human videos,” in Robotics: Science and Systems (RSS), 2020.
  26. N. García, J. Rosell, and R. Suárez, “Motion planning by demonstration with human-likeness evaluation for dual-arm robots,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 11, pp. 2298–2307, 2019.
  27. D. Ceylan, W. Li, N. J. Mitra, M. Agrawala, and M. Pauly, “Designing and fabricating mechanical automata from mocap sequences,” ACM Trans. Graph., vol. 32, no. 6, 2013.
  28. S. Coros, B. Thomaszewski, G. Noris, S. Sueda, M. Forberg, R. W. Sumner, W. Matusik, and B. Bickel, “Computational design of mechanical characters,” ACM Trans. Graph., vol. 32, no. 4, 2013.
  29. A. Kapusta and C. C. Kemp, “Optimization of robot configurations for assistive tasks,” Georgia Tech Library, 2016.
  30. A. Perez-Gracia and J. M. McCarthy, “Kinematic synthesis of spatial serial chains using clifford algebra exponentials,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 220, no. 7, pp. 953–968, 2006.
  31. S. Shirafuji and J. Ota, “Kinematic synthesis of a serial robotic manipulator by using generalized differential inverse kinematics,” IEEE Transactions on Robotics, vol. 35, no. 4, pp. 1047–1054, 2019.
  32. D. R. Gustavo H. de Rosa and J. P. Papa, “Opytimizer: A nature-inspired python optimizer,” 2019.
  33. T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, and A. Cosar, “A survey on new generation metaheuristic algorithms,” Computers & Industrial Engineering, vol. 137, p. 106040, 2019.
  34. S. M. H. Mousakazemi, “Computational effort comparison of genetic algorithm and particle swarm optimization algorithms for the proportional–integral–derivative controller tuning of a pressurized water nuclear reactor,” Annals of Nuclear Energy, vol. 136, p. 107019, 2020.

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