Abstract
The purpose of this study is to generate the gait of a two-legged robot to avoid obstacles. It is expected that two-legged robot can avoid obstacle more smoothly in the same way that animal and human adjust stride naturally to step over obstacles. Stepping points are determined optimally for walking. The gait generation problem is reduced to a combinatorial optimization problem solved by using genetic algorithm. Orbits of toes and hip between stepping points are generated by means of parametric modeling. The stable walking patterns are obtained under the condition of the maximizing walking speed and the minimizing energy consumption. The Pareto front of the multi-objective optimization for the given robot model is visualized in advance by the MOGA, the optimum walking pattern is finally determined by using the satisficing trade-off method. The effectiveness of the proposed method is shown by simulation results.
