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.
References
Gasparetto A, Zanotto V (2008) Technique for time-jerk optimal planning for robot trajectories. Robot Comput-Integr Manuf 24:415–426
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
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
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
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
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
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
Kulkarni A, Cavanaugh C (2000) Fuzzy neural network models for classification. Appl Intell 12(3):207–215
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
Cirrincione G, Cirrincione M (2003) A novel self-organizing neural network for motion segmentation. Appl Intell 18(1):27–35
Clifton C (2003) Change detection in overhead imagery using neural networks. Appl Intell 18(2):215–234
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
Bhandarkar S, Chen F (2005) Similarity analysis of video sequences using an artificial neural network. Appl Intell 22(3):251–275
Cho S, Won H (2007) Cancer classification using ensemble of neural networks with multiple significant gene subsets. Appl Intell 26(3):243–250
Shie J, Chen S (2008) Feature subset selection based on fuzzy entropy measures for handling classification problems. Appl Intell 28(1):69–82
Carcenac M (2009) A modular neural network for super-resolution of human faces. Appl Intell 30(2):168–186
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
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
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
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
Son C (2011) Intelligent robotic path finding methodologies with fuzzy/crisp entropies and learning. Int J Robot Autom 26(3):323–336
Sun S (2005) Designing approach on trajectory-tracking control of mobile robot. Robot Comput-Integr Manuf 21:81–85
Kim J, Kim S (2005) Misalignment estimation and compensation for robotic assembly with uncertainty. Robotica 23:355–364
Garcia G, Pomares J, Torres F (2009) Automatic robotic tasks in unstructured environments using an image path tracker. Control Eng Pract 17:597–608
Bang Y, Lee K (2005) Micro parts assembly system with micro gripper and RCC unit. IEEE Trans Robot 21:465–470
Kurtoglu A (2004) Flexibility analysis of two assembly lines. Robot Comput-Integr Manuf 20:247–253
Zimmermann H (1991) Fuzzy set theory and its application. Kluwer Academic, Dordrecht
Zadeh L (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28
Kosko B (1992) Neural networks and fuzzy systems. Prentice-Hall, New York
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
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
Son C (2006) Comparison of intelligent control planning algorithms for robot’s part micro-assembly task. Eng Appl Artif Intell 19:41–52
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
Author information
Authors and Affiliations
Corresponding author
Rights 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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-012-0339-y