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The usage of convergency technology for ROGA algorithm application on step walking of biped robot

이족 로봇의 계단 보행에서 Real-Coded Genetic Algorithm 의 융합 기술의 사용

  • 이정익 (인하공업전문대학 기계설계과)
  • Received : 2020.03.09
  • Accepted : 2020.05.20
  • Published : 2020.05.28

Abstract

The calculation of the optimal trajectory of the stepped top-down robot was made using a genetic algorithm and a computational torque controller. First, the total energy efficiency was minimized using the Red-Cold Generic Algorithm (RCGA) consisting of reproductive, cross, and mutation. The reproducibility condition related to the position assembly of the start and end of the stride and the joints, angles, and angular velocities are linear constraints. Next, the unequal constraint accompanies the condition for preventing the collision of the swing leg at the corner with the outer surface of the stairs, the condition of the knee joint for preventing kinematic peculiarity, and the condition of no moment in safety in the traveling direction. Finally, the angular trajectory of each joint is defined by fourth-order polynomial whose coefficient is to approximate chromosomes. This is to approximate walking. In this study, the energy efficiency of the optimal trajectory was analyzed by computer simulation through a biped robot with seven degrees of freedom composed of seven links.

계단 보행 시 로봇의 최적 궤도 계산은 유전자 알고리즘과 계산 토크 컨트롤러를 사용하여 수행되었다. 첫째, 생식, 교배, 돌연변이로 이루어진 실시간 유전 알고리즘 (RCGA)을 사용하여 총 에너지 효율이 최소화되었다. 보폭의 시작과 끝, 그리고 조인트, 각도, 각속도 위치 어셈블리 관련 재현성 조건은 선형 제약이다. 다음은 고르지 못한 제약은 코너 스윙 다리와 계단의 외부와의 충돌을 막기 위한 조건, 운동 학적 특이성을 막기 위한 무릎 관절의 조건 및 진행 방향의 안전은 보장되지 않음 이란 조건을 따른다. 마지막으로, 각 관절의 각도 궤도는 염색체를 근사 계수를 가지는 4차 다항식에 의해 정의된다. 이것은 보통 도보를 의미한다. 이 연구에서는 최적의 궤도의 에너지 효율을 7개의 링크로 구성된 7자유도의 2족 로봇을 통한 컴퓨터 시뮬레이션을 통해 분석했다.

Keywords

References

  1. J. H. Park & K. D. Kim. (1998). Biped Robot Waking Using Gravity-Compensated Inverted Pendulum Mode and Computed Torque Control. Proc. Int. Conf. on Robotics and Automation, 3528-3533.
  2. S. H. Choi, Y. H. Choi & J. G. Kim. (1999). Optimal Walking Trajectory Generation for a Biped Robot Using Genetic Algorithm. Proc. Int. Conf. on Intelligent Robots and Systems, 1456-1461.
  3. M. Y. Cheng & C. S. Lin. (1995). Genetic Algorithm for Control Design of Biped Locomotion. IEEE, 1315-1325.
  4. J. H. Park & M. S. Choi. (2004). Generation of An Optimal Gait Trajectory for Biped Robots Using A Genetic Algorithm. JSME International Journal .
  5. C. Chevallereau, A. Formal'sky & B. Perrin. (1998). Low Energy Cost Reference Trajectories for a Biped Robot. Proc. Int. Conf. on Robotics and Automation , 1398-1404.
  6. C. L. Shih. (1999). Ascending and Descending Stairs for a Biped Robot. IEEE Transactions on Systems, Man, and Cybernetics-Part A : Systems and Humans, 29(3), 255-268. https://doi.org/10.1109/3468.759271
  7. K. S. Jeon & J. H. Park. (2003). Energy Optimization of a Biped Robot for Walking a Staircase Using Genetic Algorithms. Proc. Int. Conf. on Control, Automation and Systems, 215-219.
  8. S. Tzafestas, M. Raibert & C. Tzafestas. (1996). Robust Sliding-mode Control Applied to a 5-Link Biped Robot, Journal of Intelligent and Robotic Systems 15, 67-133. https://doi.org/10.1007/BF00435728
  9. J. Furusho & A. Sano. (1990). Sensor-based control of a nine-link biped, Int. J. of Robotics Research, 9(2), 83-98. https://doi.org/10.1177/027836499000900207
  10. D. E. Goldberg. (1989). Genetic Algorithm in Search, Optimization, and Machine Learning, Addison Wesley.
  11. G. G. Jin. (2002). Genetic Algorithms and Their Applications, Kyo Woo Sa.
  12. K. M. Nam, B. S. Kim, D. K. Ko, G. R. Kim & S. G. Lee. (2009). Straight walking elevation of Biped Robot by using vision. Proc. Conf. Society of Precision Engineering, 153-154.
  13. K. Hirai & M. Hirise. (1998). The Development of Honda Humanoid Robot, Proc. of Int. Conf. on Robotics and Automation, 2, 1321-1326.
  14. Ogura, Y. Shimomura, K Kondo, A. Morishima, A. Okubo, T. Momoki, H. O. Lim & A. Takanish. (2006). Human-like Walking with Knee Stretched, Heel-contrast and Toe-off Motion, by a Humanoid Robot, Intelligent Robots and Systems, IEEE.RSJ Intelligent Robots and System.
  15. R. C. Luo & T. M. Chen. (2000). Autonomous Mobile Target tracking system based on grey-fuzzy control algorithm, IEEE Trans. Industrial Electronics, 47(4), 920-931. https://doi.org/10.1109/41.857973