Comparison of assembly-jam learning algorithms with fuzzy entropy measure for intelligent robot’s part micro-assembly

  • Changman SonEmail author


Two assembly-jam learning algorithms are introduced for reducing the task performance time as well as getting out of an assembly-jam in a robot’s part micro-assembly. The two algorithms are then compared from the viewpoint of five factors. A comprehensive comparison of the results with other recent methods and discussions are also described. The two algorithms split or unify to simplify regions situated near a similar assembly-jam state on the assembly-jam location map. This allows the part micro-assembly task to be continuously reiterated such that the speed of task performance will be faster. The task for getting out of the assembly-jam is achieved by the fewest number of robot joint control motions. This is generated by a technique to minimize the number of control motions of the 1st algorithm, without wasting time and energy. Meanwhile, the number of the last formed regions is minimized by the region unifying process of the 2nd algorithm. The two assembly-jam learning algorithms significantly reduce the task performance time in micro-assembly processes. The degree of uncertainty (measured by a fuzzy entropy function) associated with the task for getting out of the assembly-jam is used as a criterion to determine the most valid plan for a present input. The results show that the task for getting out of the assembly-jam can be successfully achieved by the control plans generated by the two assembly-jam learning algorithms.


Comparison (assembly-jam learning algorithms) Task performance time Minimizing control motions Regions minimizing algorithm Measuring uncertainty Fuzzy entropy 



  1. 1.
    Das A, Popa D (2011) Precision evaluation of modular multiscale robots for peg-in-hole microassembly tasks. In: IEEE international conference on intelligent robots and systems, pp 1699–1704Google Scholar
  2. 2.
    Kurtoglu A (2004) Flexibility analysis of two assembly lines. Robot Comput Integr Manuf 20:247–253CrossRefGoogle Scholar
  3. 3.
    Jasim I, Plapper P (2014) Contact-state monitoring of force-guided robotic assembly tasks using expectation maximization-based gaussian mixtures models. Int J Adv Manuf Technol 73:623–633CrossRefGoogle Scholar
  4. 4.
    Xiao D, Ghosh B (2004) Real-time integration of sensing, planning and control in robotic work-cells. Control Eng Pract 12:653–663CrossRefGoogle Scholar
  5. 5.
    Liao X, Wang G (2005) Employing fractals and FEM for detailed variation analysis of non-rigid assemblies. Int J Mach Tools Manuf 45:445–454CrossRefGoogle Scholar
  6. 6.
    Tang Y, Zhang Z, Ye X, Jin X, Zhang X (2013) Robotic automatic assembly for quasi-LIGA microparts. In: IEEE international conference on information and automation, pp 1076–1081Google Scholar
  7. 7.
    Kim J, Kim S (2005) Misalignment estimation and compensation for robotic assembly with uncertainty. Robotica 23:355–364CrossRefGoogle Scholar
  8. 8.
    Arkin R, Book W (1991) Intelligent material handling systems–mobile manipulation intelligent sensor-based robotic strategies for material handling. GaTech. ReportGoogle Scholar
  9. 9.
    Vandemotte S, Chriette A, Suarez Roos A, Martinet P (2016) Performing assembly task under constraints using 3D sensor-based control. Adv Intell Syst Comput 302:1389–1399Google Scholar
  10. 10.
    Minzu V, Henrioud J (1993) Systematic method for the design of flexible assembly systems. In: Proceedings of the IEEE international conference on robotics and automation, pp 56–62Google Scholar
  11. 11.
    Jakovljevic Z, Petrovic P, Hodolic J (2012) Contact states recognition in robotic part mating based on support vector machines. Int J Adv Manuf Technol 59:377–395CrossRefGoogle Scholar
  12. 12.
    Son C (1997) Optimal planning technique with a fuzzy coordinator for an intelligent robot’s part assembly. IEE Proc Control Theory Appl 144:45–52CrossRefGoogle Scholar
  13. 13.
    Son C (2004) Stability analysis and motion planning strategies with fuzzy coordinator and learning for mobile base robotic part macro/micro-assembly tasks. Int J Mach Tools Manuf 44:1683–1695CrossRefGoogle Scholar
  14. 14.
    Son C (2006) Comparison of optimal motion planning algorithms for intelligent control of robotic part micro-assembly task. Int J Mach Tools Manuf 46:508–517CrossRefGoogle Scholar
  15. 15.
    Son C (2006) Comparison of intelligent control planning algorithms for robot’s part micro-assembly task. Eng Appl Artif Intell 19:41–52CrossRefGoogle Scholar
  16. 16.
    Son C (2011) Intelligent robotic path finding methodologies with fuzzy/crisp entropies and learning. Int J Robot Autom 26:323–336Google Scholar
  17. 17.
    Son C (2014) Intelligent jamming region division with machine learning and fuzzy optimization for control of robot’s part micro-manipulative task. Inf Sci 256:211–224MathSciNetCrossRefGoogle Scholar
  18. 18.
    Chen H, Cheng H, Liu J, Zhang B, Zhang G, Fuhlbrigge T (2013) Performance improvement for high accuracy assembly process in manufacturing automation. In: IEEE international conference on automation science and engineering, pp 540–545Google Scholar
  19. 19.
    Jayaveera N, Webb P (2007) Adaptive robotic assembly of compliant aero-structure components. Robot Comput Integr Manuf 23:180–194CrossRefGoogle Scholar
  20. 20.
    Lin L, Yang Y, Song Y, Nemec B, Ude A, Rytz J, Buch A, Kruger N, Savarimuthu T (2015) Peg-in-hole assembly under uncertain pose estimation. In: Proceedings of the world congress on intelligent control and automation, pp 2842–2847Google Scholar
  21. 21.
    Jasim I, Plapper P, Voos H (2014) Position identification in force-guided robotic peg-in-hole assembly tasks, pp 217–222Google Scholar
  22. 22.
    Jeddisaravi K, Alitappeh R, Pimenta L (2016) Multi-objective approach for robot motion planning in search tasks. Appl Intell 45:305–321CrossRefGoogle Scholar
  23. 23.
    Dai X, Jiang L, Zhao Y (2016) Cooperative exploration based on supervisory control of multi-robot systems. Appl Intell 45:18–29CrossRefGoogle Scholar
  24. 24.
    Wei C, Hindriks K, Jonker C (2016) Altruistic coordination for multi-robot cooperative pathfinding. Appl Intell 44:269–281CrossRefGoogle Scholar
  25. 25.
    Zhang Q, Wang R, Yang J, Ding K, Li Y, Hu J (2018) Modified collective decision optimization algorithm with application in trajectory planning of UVA. Appl Intell 48:2328–2354CrossRefGoogle Scholar
  26. 26.
    Saric A, Xiao J, Shi J (2013) Robotic surface assembly via contact state transitions. In: IEEE international conference on automation science and engineering, pp 954–959Google Scholar
  27. 27.
    Balamurali G, Deepak B, Bahubalendruni C (2018) An optimal robotic assembly sequence planning by assembly subsets detection method using teaching learning-based optimization algorithm. IEEE Trans Autom Sci Eng 15:1369–1385CrossRefGoogle Scholar
  28. 28.
    Wang Y, Xiong R, Yu H, Zhang J, Liu Y (2018) Perception of demonstration for automatic programing of robotic assembly: framework, algorithm, and validation. IEEE/ASME Trans Mechatron 23:1059–1070CrossRefGoogle Scholar
  29. 29.
    Wyk K, Culleton M, Falco J, Kelly K (2018) Comparative peg-in-hole testing of a force-based manipulation controlled robotic hand. IEEE Trans Robot 34:542–549CrossRefGoogle Scholar
  30. 30.
    Chang Y, Lin C, Yang J (2018) Cyber-physical-robotic system (CPRS)-based modeling and execution of assembly tasks. Sens Mater 30:1919–1933Google Scholar
  31. 31.
    Lee D, Na M, Song J, Park C, Park D (2019) Assembly process monitoring algorithm using force data and deformation data. Robot Comput Integr Manuf 56:149–156CrossRefGoogle Scholar
  32. 32.
    Wyk K, Marvel J (2018) Strategies for improving and evaluating robot registration performance. IEEE Trans Autom Sci Eng 15:330–328Google Scholar
  33. 33.
    Zhang W, Wang J, Lin Y (2019) Integrated design and operation management for enterprise systems. Enterp Inf Syst 13:424–429CrossRefGoogle Scholar
  34. 34.
    Zhang W, Luttervelt C (2011) Towards a resilient manufacturing system. CIRP Ann-Manuf Technol 60:469–472CrossRefGoogle Scholar
  35. 35.
    Zhang T, Zhang W, Gupta M (2018) An underactuated self-reconfigurable robot and the reconfiguration evolution. Mech Mach Theory 124:248–258CrossRefGoogle Scholar
  36. 36.
    Whitney D (1982) Quasi-static assembly of compliantly supported rigid parts. J Dyn Syst Meas Control 104:65–77CrossRefGoogle Scholar
  37. 37.
    Kosko B (1992) Neural networks and fuzzy systems. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  38. 38.
    Zimmermann H (1991) Fuzzy set theory and its application. Kluwer Academic Publishers, Englewood CiffsCrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic and Electrical EngineeringDanKook UniversityYonginSouth Korea

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