Computer
Genetic Algorithms
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This paper proposes a dynamically stable and optimal trajectory generation method for biped robots to walk up and down stairs, based on human motion analysis, since a human walks efficiently without high energy consumption, and the energy-efficient locomotion pattern results in a more natural walking pattern. Seven important elements of the human gait on stairs are identified in the analysis of the motion data captured from subjects. Those factors enable us to generate trajectories of biped robots similar to that of human beings walking up-and-down stairs. The dynamics of the robot and human are different in weight distribution, degree of freedom and so on. A real-coded genetic algorithm as an optimization tool is used to produce the optimized gait for the robot and to improve the energy autonomy and stability. Various computer simulations were performed based on a 12-DOF biped robot model with which many of the essential characteristics of the human walking motion on stairs can be captured. The proposed method exhibits its efficiency in quickly finding an optimal trajectory, which is due to not only the nature of genetic algorithms but also a small number of design variables employed. Thus, this makes it possible to generate various locomotion trajectories of biped robots simply by appropriately changing some of the boundary conditions.