Optimal Trajectory Generation for Biped Robots Walking Up-and-Down Stairs

  • Kwon O-Hung (Department of Precision Mechanical Engineering, Hanyang University) ;
  • Jeon Kweon-Soo (Department of Precision Mechanical Engineering, Hanyang University) ;
  • Park Jong-Hyeon (School of Mechanical Engineering, Hanyang University)
  • 발행 : 2006.05.01

초록

This paper proposes an optimal trajectory generation method for biped robots for walking up-and-down stairs using a Real-Coded Genetic Algorithm (RCGA). The RCGA is most effective in minimizing the total consumption energy of a multi-dof biped robot. Each joint angle trajectory is defined as a 4-th order polynomial of which the coefficients are chromosomes or design variables to approximate the walking gait. Constraints are divided into equalities and inequalities. First, equality constraints consist of initial conditions and repeatability conditions with respect to each joint angle and angular velocity at the start and end of a stride period. Next, inequality constraints include collision prevention conditions of a swing leg, singular prevention conditions, and stability conditions. The effectiveness of the proposed optimal trajectory is shown in computer simulations with a 6-dof biped robot model that consists of seven links in the sagittal plane. The optimal trajectory is more efficient than that generated by the Modified Gravity-Compensated Inverted Pendulum Mode (MGCIPM). And various trajectories generated by the proposed GA method are analyzed from the viewpoint of the consumption energy: walking on even ground, ascending stairs, and descending stairs.

키워드

참고문헌

  1. Cheng, M. Y. and Lin, C. S., 1995, 'Genetic Algorithm for Control Design of Biped Locomotion,' IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 1315-1320 https://doi.org/10.1109/ICSMC.1995.537954
  2. Chevallereau, C., Formal'sky, A. and Perrin, B., 1998, 'Low Energy Cost Reference Trajectories for a Biped Robot,' IEEE Int. Conf. on Robotics and Automation, pp. 1398-1404 https://doi.org/10.1109/ROBOT.1998.677300
  3. Choi, S. H., Choi, Y. H. and Kim, J. G., 1999, 'Optimal Walking Trajectory Generation for a biped Robot Using Genetic Algorithm,' IEEE Int. Conf. on Intelligent Robots and Systems, pp. 1456-1461 https://doi.org/10.1109/IROS.1999.811684
  4. Furusho, J. and Sano, A., 1990, 'Sensor-based control of a nine-link biped,' Int. J. of Robotics Research, Vol. 9, No. 2, pp. 83-98 https://doi.org/10.1177/027836499000900207
  5. Goldberg, D. E., 1989, Genetic Algorithm in Search, Optimization, and Machine Learning, Addison Wesley
  6. Hwang, Y., Inohira, E., Konno, A. and Uchiyama, M., 2003, 'An Order n Dynamic Simulator for a Humanoid Robot with a Virtual Spring-Damper Contact Model,' IEEE Int. Conf. on Robotics and Automation, pp. 31-36
  7. Jin, G. G., 2002, Genetic Algorithms and Their Applications, Kyo Woo Sa, Korea
  8. Park, J. H. and Choi, M. S., 2004, 'Generation of An Optimal Gait Trajectory for Biped Robots Using A Genetic Algorithm,' JSME International Journal, Series C, Vol. 47, No. 2, pp. 715-721 https://doi.org/10.1299/jsmec.47.715
  9. Park, J. H. and Kim, K. D., 1998, 'Biped Robot Walking Using Gravity-Compensated Inverted Pendulum Mode and Computed Torque Control,' IEEE Int. Conf. on Robotics and Automation, pp. 3528-3533 https://doi.org/10.1109/ROBOT.1998.680985
  10. Peng, C. and ONO, K., 2005, 'Accuracy, Analysis of Optimal Trajectory Planning Methods Based on Function Approximation for a Four-DOF Biped Walking Model,' Journal of Mechanical Science and Technology, Vol. 19, No. 1, pp. 452-460 https://doi.org/10.1007/BF02916167
  11. Shih, C. L., 1999, 'Ascending and Descending Stairs for a Biped Robot,' IEEE Transactions on ?Systems, Man, and Cybernetics-Part A : Systems and Humans, Vol. 29, No. 3, pp. 255-268 https://doi.org/10.1109/3468.759271
  12. Tzafestas, S., Raibert, M. and Tzafestas, C., 1996, 'Robust Sliding-mode Control Applied to a 5-Link Biped Robot,' Journal of Intelligent and Robotic Systems, 15, pp. 67-133 https://doi.org/10.1007/BF00435728
  13. Zalzala, A.M.S. and Fleming, P.J., 1997, 'Genetic algorithms in engineering systems,' lEE