Bioinspired Straight Walking Task-Space Planner
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Although the attention on bipedal locomotion has increased over the last decades, robots are still far behind compared to human locomotor abilities. Their performance limitations can be partially attributed to the hardware, but the primary constrain has been the poor understanding of bipedal dynamics. Based on the 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 potential energy model and employs locomotor strategies obtained from human data as a reference for optimal behaviour. The model generates CoM trajectory, foot swing trajectory and the base of support from the knowledge of the desired speed, initial posture, height, weight, 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. Although the planned trajectory is not smooth compared to human trajectories, the proposed model significantly reduces the error in the estimation of the CoM vertical trajectory. Moreover, being the planner able to generate a single stride in less than 140 ms and sequences of 10 strides in less than 600 ms, it allows an online task-space planning for locomotion. Lastly, the proposed architecture is also supported by analogies with current theories on human motor control of locomotion.
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