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Quasi-Natural Humanoid Robot Walking Trajectory Generator Based on Five-Mass With Angular Momentum Model

This paper proposes to develop a quasi-natural humanoid robot walking trajectory generator based on five-mass with angular momentum model using feedback–feedforward controller. This approach aims to minimize modeling error and improve the frequency characteristics from nonminimum phase properties so that walking performance and tracking accuracy are enhanced. This proposed model focuses on the angular momentum effects from arm and leg rotation to reduce modeling error to enhance walking performance. Based on pole-zero cancelation using series approximation method, it can overcome the sudden change of the natural zero-moment point reference due to the frequency characteristics in the nonminimum phase control system. The humanoid walking pattern generator is verified and demonstrated using a humanoid robot developed in our laboratory based on the proposed model.