Mole-inspired Forepaw Design and Optimization Based on Resistive Force Theory  

作　　者：Tao Zhang Zhaofeng Liang Hongmin Zheng Zibiao Chen Kunquan Zheng Ran Xu Jiabin Liu Haifei Zhu Yisheng Guan Kun Xu Xilun Ding 

机构地区：[1]School of Mechanical Engineering and Automation,Beihang University,Beijing,100191,China [2]School of Electro-mechanical Engineering,Guangdong University of Technology,Guangzhou,510006,China

出　　处：《Journal of Bionic Engineering》2025年第1期171-180,共10页仿生工程学报(英文版)

基　　金：financially supported in-part by the National Natural Science Foundation of China(52275011);the Natural Science Foundation of Guangdong Province(2023B1515020080);the Natural Science Foundation of Guangzhou(2024A04J2552);the Fundamental Research Funds for the Central Universities,the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(CAST)(2021QNRC001);the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011253);the Higher Education Institution Featured Innovation Project of Department of Education of Guangdong Province(GrantNo.2023KTSCX138).

摘　　要：Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.

关 键 词：Resistive force theory Mole-inspired forepaw design Structural optimization Bioinspired robot 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]