Walking is like slithering: A unifying, data-driven view of locomotion

Abstract

Legged movement is ubiquitous in nature and of increasing interest for robotics. Most legged animals routinely encounter foot slipping, yet detailed modeling of multiple contacts with slipping exceeds current simulation capacity. Here we present a principle that unifies multilegged walking (including that involving slipping) with slithering and Stokesian (low Reynolds number) swimming. We generated data-driven principally kinematic models of locomotion for walking in low-slip animals (Argentine ant, 4.7% slip ratio of slipping to total motion) and for high-slip robotic systems (BigANT hexapod, slip ratio 12 to 22%; Multipod robots ranging from 6 to 12 legs, slip ratio 40 to 100%). We found that principally kinematic models could explain much of the variability in body velocity and turning rate using body shape and could predict walking behaviors outside the training data. Most remarkably, walking was principally kinematic irrespective of leg number, foot slipping, and turning rate. We find that grounded walking, with or without slipping, is governed by principally kinematic equations of motion, functionally similar to frictional swimming and slithering. Geometric mechanics thus leads to a unified model for swimming, slithering, and walking. Such commonality may shed light on the evolutionary origins of animal locomotion control and offer new approaches for robotic locomotion and motion planning.

Multilegged matter transport: A framework for locomotion on noisy landscapes

“…As demonstrated in previous studies of dissipation-dominated multilegged locomotion on flat ground (27,28), because of the periodic limb lifting and landing, an effective viscous (rate-dependent) cycle-averaged thrust-velocity (the average thrust and velocity over a period, respectively) relationship emerges in frictional environments, despite such thrusts being instantaneously independent of velocity. Specifically, the relationship of cycle-averaged robot locomotion velocity is derived to be linearly correlated with the cycle-averaged thrust: v ¼ g À1f , where g is the effective viscous drag coefficient (Fig.…”

“…Self-propulsion with (I) nominal contact and (II) contact errors are compared. ( B ) The instantaneous thrust f ( t ) as a function of time, derived from ( 28 ). ( C ) The cumulative distribution function (CDF) of τ u .…”

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination

“…Data-driven [40] [187] [188] [189] [190] [191][192] [193] [194] SDE [195] [196] [197][198] [199] [200] [201]…”

“…Modeling PHSs based on first principles is challenging when there are random disturbances in GRF. Based on this representation, data-driven methods are also applied to cope with walking movements [194], GRFs [187], and daily living [188].…”