Interactive evolutionary computation for robot design support system

Recently, human-friendly support systems for product design have been developed as demands of individual user to products become diversified. In the previous works, Interactive Evolutionary Computation has been applied to support product designs. An advantage of Interactive Evolutionary Computation is that it can perform the optimization based on human preference and feeling in addition to the evaluation by fitness functions. However, the main target of the application of Interactive Evolutionary Computation lies in the design of appearance, not functionality. The simultaneous optimization of appearance and functions of a target product is very important to shorten the cycle of product development. Therefore, in this study, the final aim is to develop a support system for simultaneous design of appearance, shape, and functionality. First of all, in this paper, we focus on the development of a design system based on the user's preference. We apply Interactive Evolutionary Computation to make designs based on the human preference and feeling. As an example, we apply a proposed method to design a partner robot because the systematic tools of developing robots for daily use have rarely been developed. This system aims not to make final design but to make “Relation between the user and the robot.” Next, we focus on the development of a simulation system to confirm the results of the developed. We can see images of the robot and movements, etc. by using this system, even if we do not actually make robots. Finally, we discuss the effectiveness of the proposed methods and future works.

[1]  Fumio Kojima,et al.  Fuzzy and Neural Computing for Communication of a Partner Robot , 2003, J. Multiple Valued Log. Soft Comput..

[2]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic , 1995 .

[3]  Aude Billard,et al.  Stochastic gesture production and recognition model for a humanoid robot , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Jacek M. Zurada,et al.  Computational Intelligence: Imitating Life , 1994 .

[6]  L. Steels.  Evolving grounded communication for robots , 2003, Trends in Cognitive Sciences.

[7]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[8]  Fumio Kojima,et al.  Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot , 2006, Soft Comput..

[9]  Tomoyuki Hiroyasu,et al.  Extraction of design variables using collaborative filtering for interactive genetic algorithms , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[10]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[11]  Takanori Shibata,et al.  Psychological and Social Effects of One Year Robot Assisted Activity on Elderly People at a Health Service Facility for the Aged , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Subrata Biswas,et al.  The Future of Competition: Co-Creating Unique Value with Customers , 2004 .

[13]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[14]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic - basic concepts and applications , 1997 .

[15]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .