Skip to main content

Advertisement

Log in

Similarity measuring strategy of image patterns based on fuzzy entropy and energy variations in intelligent robot’s manipulative task

Applied Intelligence Aims and scope Submit manuscript

Abstract

A similarity measuring strategy of image patterns based on fuzzy entropy and energy variations, using an intelligent robot’s part macro-assembly (part-bringing) as an example, is presented. A part macro-assembly, locating various shaped assembly holes (targets) in a workspace corresponding to shapes of parts and then bringing a part to a corresponding target for the purpose of part mating despite existing obstalces, is introduced. This is accomplished by cooperating a neural network system with a fuzzy optimal control. Fuzzy entropy and energy functions, which are useful measures of variability and information in terms of uncertainty, are introduced to measure its overall performance of task execution related to the part-bringing task. An interrelation among learning, fuzzy entropy, and energy variations used as a measuring tool for a degree of similarity of image patterns is described. Through variations of fuzzy entropy and energy, a degree of similarity between input and desired output image patterns of neural network can be measured. The proposed technique is not only a useful tool to measure a degree of similarity between image patterns, but applicable to a wide range of robotic tasks including motion planning, manufacturing, maneuvering around workspace, and part mating with various shaped parts and targets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Gasparetto A, Zanotto V (2008) Technique for time-jerk optimal planning for robot trajectories. Robot Comput-Integr Manuf 24:415–426

    Article  Google Scholar 

  2. Liao X, Wang G (2005) Employing fractals and FEM for detailed variation analysis of non-rigid assemblies. Int J Mach Tools Manuf 45:445–454

    Article  Google Scholar 

  3. Pratihar D, Deb K, Ghosh A (1999) Fuzzy-genetic algorithms and time-optimal obstacle-free path generation for mobile robots. Eng Optim 32(1):145–172

    Article  Google Scholar 

  4. Hui N, Mahendar V, Pratihar D (2006) Time-optimal collision-free navigation of a car-like mobile robot using a neuro-fuzzy approach. Fuzzy Sets Syst 157(16):2171–2204

    Article  MathSciNet  MATH  Google Scholar 

  5. Lee S, Kim J (1997) Augmenting the discrimination power of HMM by NN for on-line cursive script recognition. Appl Intell 7(4):305–314

    Article  Google Scholar 

  6. Chohra A, Farah A, Benmehrez C (1998) Neural navigation approach for intelligent autonomous vehicles (IAV) in partially structured environments. Appl Intell 8(3):219–233

    Article  Google Scholar 

  7. Gonzalez A, Grãna M, D’Anjou A, Albizuri F, Torrealdea F (1998) A comparison of experimental results with an evolution strategy and competitive neural networks for near real-time color quantization of image sequences. Appl Intell 8(1):43–51

    Article  Google Scholar 

  8. Kulkarni A, Cavanaugh C (2000) Fuzzy neural network models for classification. Appl Intell 12(3):207–215

    Article  Google Scholar 

  9. Azouaoui Q, Chohra A (2002) Soft computing based pattern classifiers for the obstacle avoidance behavior of intelligent autonomous vehicles (IAV). Appl Intell 16(3):249–272

    Article  MATH  Google Scholar 

  10. Cirrincione G, Cirrincione M (2003) A novel self-organizing neural network for motion segmentation. Appl Intell 18(1):27–35

    Article  MATH  Google Scholar 

  11. Clifton C (2003) Change detection in overhead imagery using neural networks. Appl Intell 18(2):215–234

    Article  MATH  Google Scholar 

  12. Aguirre E, González A (2003) A fuzzy perceptual model for ultrasound sensors applied to intelligent navigation of mobile robots. Appl Intell 19(3):171–187

    Article  Google Scholar 

  13. Bhandarkar S, Chen F (2005) Similarity analysis of video sequences using an artificial neural network. Appl Intell 22(3):251–275

    Article  MATH  Google Scholar 

  14. Cho S, Won H (2007) Cancer classification using ensemble of neural networks with multiple significant gene subsets. Appl Intell 26(3):243–250

    Article  MATH  Google Scholar 

  15. Shie J, Chen S (2008) Feature subset selection based on fuzzy entropy measures for handling classification problems. Appl Intell 28(1):69–82

    Article  Google Scholar 

  16. Carcenac M (2009) A modular neural network for super-resolution of human faces. Appl Intell 30(2):168–186

    Article  Google Scholar 

  17. Park B, Pedrycz W, Oh S (2010) Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification. Appl Intell 32(1):27–46

    Article  Google Scholar 

  18. An S, Kang J, Choi W, Oh S (2011) A neural network based retrainable framework for robust object recognition with application to mobile robotics. Appl Intell 35(2):190–210

    Article  Google Scholar 

  19. Valova I, Milano G, Bowen K, Gueorguieva N (2011) Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition. Appl Intell 35(2):211–225

    Article  Google Scholar 

  20. Son C (2007) Correlation between learning (probability of success) and fuzzy entropy in control of intelligent robot’s part macro-assembly tasks with sensor fusion techniques. Robot Comput-Integr Manuf 23:47–62

    Article  Google Scholar 

  21. Son C (2011) Intelligent robotic path finding methodologies with fuzzy/crisp entropies and learning. Int J Robot Autom 26(3):323–336

    Google Scholar 

  22. Sun S (2005) Designing approach on trajectory-tracking control of mobile robot. Robot Comput-Integr Manuf 21:81–85

    Article  Google Scholar 

  23. Kim J, Kim S (2005) Misalignment estimation and compensation for robotic assembly with uncertainty. Robotica 23:355–364

    Article  Google Scholar 

  24. Garcia G, Pomares J, Torres F (2009) Automatic robotic tasks in unstructured environments using an image path tracker. Control Eng Pract 17:597–608

    Article  Google Scholar 

  25. Bang Y, Lee K (2005) Micro parts assembly system with micro gripper and RCC unit. IEEE Trans Robot 21:465–470

    Article  Google Scholar 

  26. Kurtoglu A (2004) Flexibility analysis of two assembly lines. Robot Comput-Integr Manuf 20:247–253

    Article  Google Scholar 

  27. Zimmermann H (1991) Fuzzy set theory and its application. Kluwer Academic, Dordrecht

    Google Scholar 

  28. Zadeh L (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28

    Article  MathSciNet  MATH  Google Scholar 

  29. Kosko B (1992) Neural networks and fuzzy systems. Prentice-Hall, New York

    MATH  Google Scholar 

  30. Son C (2004) Intelligent control planning strategies with neural network/fuzzy coordinator and sensor fusion for robotic part macro/micro-assembly tasks in a partially unknown environment. Int J Mach Tools Manuf 44:1667–1681

    Article  Google Scholar 

  31. Son C (2006) Comparison of optimal motion planning algorithms for intelligent control of robotic part micro-assembly task. Int J Mach Tools Manuf 46(5):508–517

    Article  MathSciNet  Google Scholar 

  32. Son C (2006) Comparison of intelligent control planning algorithms for robot’s part micro-assembly task. Eng Appl Artif Intell 19:41–52

    Article  Google Scholar 

  33. Son C (2006) Intelligent control planning strategies for mobile base robotic part macro and micro-assembly tasks. Int J Robot Autom 21(3):207–217

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Changman Son.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Son, C. Similarity measuring strategy of image patterns based on fuzzy entropy and energy variations in intelligent robot’s manipulative task. Appl Intell 38, 131–145 (2013). https://doi.org/10.1007/s10489-012-0339-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-012-0339-y

Keywords

Navigation