Wall-Climbing Performance of Gecko-inspired Robot with Soft Feet and Digits enhanced by Gravity Compensation

TL;DR

This paper introduces a gravity compensation strategy for gecko-inspired climbing robots with soft feet, significantly improving stability and success rate during ceiling walking by correcting body posture and enhancing end-effector attachment reliability.

Contribution

It proposes a feedforward gravity compensation method combined with leg coordination to address posture deviations caused by gravity in low-stiffness climbing robots, validated through experiments.

Findings

FGC strategy increased success rate from 30% to 100% in ceiling walking trials.

Robots with FGC achieved a climbing speed of 9.2 mm/s.

The method enhances stability and reliability of end-effector attachment during inverted locomotion.

Abstract

Gravitational forces can induce deviations in body posture from desired configurations in multi-legged arboreal robot locomotion with low leg stiffness, affecting the contact angle between the swing leg's end-effector and the climbing surface during the gait cycle. The relationship between desired and actual foot positions is investigated here in a leg-stiffness-enhanced model under external forces, focusing on the challenge of unreliable end-effector attachment on climbing surfaces in such robots. Inspired by the difference in ceiling attachment postures of dead and living geckos, feedforward compensation of the stance phase legs is the key to solving this problem. A feedforward gravity compensation (FGC) strategy, complemented by leg coordination, is proposed to correct gravity-influenced body posture and improve adhesion stability by reducing body inclination. The efficacy of this strategy is validated using a quadrupedal climbing robot, EF-I, as the experimental platform. Experimental validation on an inverted surface (ceiling walking) highlight the benefits of the FGC strategy, demonstrating its role in enhancing stability and ensuring reliable end-effector attachment without external assistance. In the experiment, robots without FGC only completed in 3 out of 10 trials, while robots with FGC achieved a 100\% success rate in the same trials. The speed was substantially greater with FGC, achieved 9.2 mm/s in the trot gait. This underscores the proposed potential of FGC strategy in overcoming the challenges associated with inconsistent end-effector attachment in robots with low leg stiffness, thereby facilitating stable locomotion even at inverted body attitude.