Legged Robot Trajectory Generation using Evolved Fuzzy Machine for IoT Environments

IoT 환경을 위한 진화된 퍼지머신을 이용한 로봇의 궤적생성

  • Kim, Dong Won (Dept. of Digital Electronics, Inha Technical College)
  • 김동원 (인하공업전문대학 디지털전자과)
  • Received : 2020.08.24
  • Accepted : 2020.09.09
  • Published : 2020.09.30


The Internet of Things (IoT) era, in which all items used in daily life are equipped with a network connection function, and they are closely linked to increase the convenience of life and work, has opened wide. Robots also need to develop according to the IoT environment. A use of new type of evolved fuzzy machine (EFM) for generating legged robot trajectory in IoT enviornmentms is discussed in this paper. Fuzzy system has been widely used for describing nonlinear systems. In fuzzy system, determination of antecedent and consequent structures of fuzzy model has been one of the most important problems. EFM is described which carries out evolving antecedent and consequent structure of fuzzy system for legged robot. To generate the robot trajectory, parameters of each structure in the fuzzy system are tuned automatically by the EFM. The results demonstrate the performance of the proposed approach for the legged robot.


  1. D.Kim, N.H.Kim, S.J.Seo, and G.T. Park, "Fuzzy Modeling of Zero Moment Point Trajectory for a Biped Walking Robot," Lect. Notes Artif. Int., Vol.3214, pp.716-722, 2005.
  2. M.Vukobratovic and J.Stepanenko, "On the Stability of Anthropomorphic Systems," Math. Biosci., Vol.15, pp.1-37, 1972.
  3. M.Vukobratovic and B.Brovac, "Zero-Moment Point-Thirty Five Years of Its Life," Int. J. Humanoid Robotics, Vol.1, pp.157-173, 2004.
  4. M.Vukobratovic, D.Andric and B.Borovac, "How to Achieve Various Gait Patterns from Single Nominal," Int. J Advanced Robotic Syst., Vol.1, No.2, pp.99-108, 2004.
  5. T.Takagi and M.Sugeno, "Fuzzy Identification of Systems and Its Applications to Modeling and Control," IEEE Trans. Syst., Man, Cybern., SMC-15, pp.116-132, 1985.
  6. M.Setnes and H.Roubos, "GA-Fuzzy Modeling and Classification: Complexity and Performance," IEEE Trans. Fuzzy Syst., Vol.8, No.5, pp.509-522, 2000.
  7. Y.Shi, R.Eberhart and Y.Chen, "Implementation of Evolutionary Fuzzy Systems," IEEE Trans. Fuzzy Syst., Vol.7, No.2, pp.109-119, 1999.
  8. S.Matsushita, T.Furuhashi, H.Tsutsui, and Y.Uchikawa, "Efficient Search for Fuzzy Models using Genetic Algorithm," Information Sciences, Vol.110, pp.41-50, 1998.
  9. L.Shimojima, T.Fukuda and Y.Hasegawa, "Self-tuning Fuzzy Modeling with Adaptive Membership Function, Rules, and Hierarchical Structure based on Genetic Algorithm," Fuzzy Sets and Systems, Vol.71, No.3, pp.295-309, 1995.
  10. D.W.Kim, "Fused Fuzzy Logic System for Corrupted Time Series Data Analysis," The JKIOTS Transactions, Vol.4, No.1, pp.1-6, 2018.
  11. J.Holland, "Adaptation in Natural and Artificial Systems," The University of Michigan Press, Ann Arbor, M.I., 1975.
  12. D.W.Kim, G.-T.Park, "Optimization of Polynomial Neural Networks: An Evolutionary Approach," The Korean Institute of Electrical Engineers, Vol.52, No.7, pp.421-433, 2003.
  13. D.W.Kim, G.T.Park, "A Novel Design of Self-organizing Approximator Technique: an Evolutionary Approach," Systems, Man and Cybernetics, 2003. IEEE International Conference on Vol.5, pp.4643-4648, 2003.
  14. D.W.Kim, "Kinematic Based Walking Pattern of Biped Robot," The JKIOTS Transactions, Vol.4, No.2, pp.7-12, 2018.
  15. D.Kim, S.H,Huh and G.T.Park, "Modeling Corrupted Time Series Data via Nonsingleton Fuzzy Logic System," Lect. Notes Comput. Sc, Vol.3316, pp.1298-1303, 2004.