Online Joint Trajectory Generation of Human-like Biped Walking

Biped walking has long been studied in the area of gait analysis and robotic locomotion. The goal of this paper is to establish a systematic methodology for human-like natural walking by fusing the measured human joint data and optimal pattern generation techniques based on a full-body humanoid model. To this end, this paper proposes an adaptive two-stage gait pattern by which the step length and walking velocity can be changed with two scaling factors. In addition, to cope with the situations involving passing over a small obstacle, the joint trajectories of the swing foot can be adjusted with a novel concept of differential angle trajectory using a reliable optimization method, viz. particle swarm optimization. The feasibility of the proposed walking scheme is validated by walking experiments with the robot platform DARwIn-OP.