通过软磁材料减少附着力的可控磁性爬墙轮

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Yang Tian;Hayato Jitsukawa;Shugen Ma;Guoteng Zhang
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引用次数: 0

摘要

随着桥梁和工厂设施等关键基础设施的老化,开发使用永磁体的创新型爬墙机器人变得越来越重要。此类机器人的传统设计依赖于控制升降器或永磁体的位置来控制附着状况,这带来了严重的安全问题,包括表面过渡时附着力不一致以及失去控制时的坠落风险。为了克服这些问题,本文介绍了一种新型磁轮设计,它利用软磁材料(SMM)来控制特定方向粘附力的降低。通过对各种磁铁和软磁材料配置的比较分析,展示了其对粘附力的影响。在分析的基础上,提供了一种轮毂设计,并对 SMM 覆盖区域的效果进行了研究。为了验证减少附着力的有效性,介绍并分析了使用所建议的轮子来实现爬墙任务的机器人。在实验中,配备了拟议轮子设计的原型机器人在受控条件下执行爬墙任务时表现出更高的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Magnetic Wall-Climbing Wheels With Controllable Adhesion Reduction via Soft Magnetic Material
With the aging of critical infrastructure like bridges and plant facilities, the development of innovative wall-climbing robots using permanent magnets has become increasingly important. Traditional designs of such robots depend on controlling the position of lifters or permanent magnets to control the adhesion condition, which introduces significant safety concerns, including inconsistent adhesion during surface transitions and the risk of falls when control is lost. To overcome these issues, this paper introduces a novel magnet wheel design that utilizes Soft Magnetic Material (SMM) to control the reduction of adhesive force in a specific direction. The effect on adhesion was shown with a comparative analysis of various magnet and SMM configurations. Based on the analyses, a wheel design was provided with investigating the effect of the SMM cover region. For verifying the effectiveness of the adhesion reduction, a robot with the proposed wheel is presented and analyzed to realize wall-climbing tasks. In experiments, a prototype robot equipped with the proposed wheel design demonstrates enhanced safety for wall-climbing tasks under controlled conditions.
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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