Abstract
Despite the improvements in humanoids robots over the last few decades, they are still far behind compared to human locomotor abilities. Their performance limitations can be partially attributed to the hardware, but the primary constraint has been the modelling and understanding of bipedal dynamics. Based on a recently developed model of potential energy for bipedal structures, this work proposes a task-space planner for human-like straight locomotion. The proposed architecture is based on the potential energy model and employs locomotor strategies from human data as a reference for human behaviour. The model generates Centre of Mass (CoM) trajectories, foot swing trajectories and the Base of Support (BoS) over time. Their calculation relies on the knowledge of the desired speed, initial posture, height, mass, number of steps and the angle between the foot and the ground during heel-strike. The data show that the proposed architecture can generate behaviour in line with human walking strategies for both the CoM and the foot swing. Despite the CoM vertical trajectory being not as smooth as a human trajectory, yet the proposed model significantly reduces the error in the estimation of the CoM vertical trajectory compared to the inverted pendulum models. The proposed planner is well suited for online task-space planning for locomotion as it can generate a single stride in less than 140 ms and sequences of 10 strides in less than 600 ms.
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URL
http://arxiv.org/abs/1808.10799