Wall-Climbing Performance of Gecko-inspired Robot with Soft Feet and Digits enhanced by Gravity Compensation
<|reference_start|>Wall-Climbing Performance of Gecko-inspired Robot with Soft Feet and Digits enhanced by Gravity Compensation: 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.<|reference_end|>
@article{wang2024wall-climbing,
    title={Wall-Climbing Performance of Gecko-inspired Robot with Soft Feet and
  Digits enhanced by Gravity Compensation},
    author={Bingcheng Wang, Zhiyuan Weng, Haoyu Wang, Shuangjie Wang, Zhouyi Wang,
  Zhendong Dai, Ardian Jusufi},
    journal={arXiv preprint arXiv:2405.02639},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02639},
    primaryClass={cs.RO}
}