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一种由自感知控制的扭曲人工肌肉驱动的多运动仿生软体六足机器人。

A multi-motion bionic soft hexapod robot driven by self-sensing controlled twisted artificial muscles.

机构信息

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, People's Republic of China.

出版信息

Bioinspir Biomim. 2021 Jun 18;16(4). doi: 10.1088/1748-3190/ac0121.

DOI:10.1088/1748-3190/ac0121
PMID:33984843
Abstract

Artificial muscles have unique advantages for driving bionic robots because their driving mode is similar to biological muscles. However, there is still a big gap between the existing artificial muscle and biological muscle in performance. The twisted artificial muscles (TAMs) from nylon 6,6 provides a low-cost, high integration, low hysteresis driving method. But as a soft actuator, the control of the TAM is so complex that the advantage of excellent embeddedness has not been brought into play. This work presents a self-sensing control method for the TAM by monitoring the real-time resistance of the heating wire which realizes the accurate controlling of the TAM temperature. The simultaneous control of 18 TAMs is realized by using the self-sensing control method. By using a new step walking method based on the principle of insect bionics, a bionic soft hexapod robot with both multi-motion and load capacity is realized. Besides, due to the excellent environmental adaptability of the TAM, the bionic robot can realize amphibious motion both on land and underwater conditions, and the corresponding maximum load capacities are 300 g and 1 kg, respectively. This work not only provides a reliable self-sensing control method of the TAMs but also promotes the development of bionic soft robots.

摘要

人工肌肉因其驱动方式类似于生物肌肉,因此在驱动仿生机器人方面具有独特的优势。然而,现有的人造肌肉在性能上与生物肌肉仍存在很大差距。来自尼龙 6,6 的扭曲人工肌肉(TAM)提供了一种低成本、高集成、低滞后的驱动方法。但是,作为一种软执行器,TAM 的控制非常复杂,其出色的嵌入式优势并未得到发挥。本工作通过监测加热丝的实时电阻来实现 TAM 温度的精确控制,提出了一种 TAM 的自感测控制方法。通过使用自感测控制方法,实现了对 18 个 TAM 的同步控制。通过采用基于昆虫仿生学原理的新型步行动作方法,实现了具有多运动和承载能力的仿生软六足机器人。此外,由于 TAM 具有优异的环境适应性,仿生机器人可以在陆地和水下条件下实现两栖运动,相应的最大承载能力分别为 300g 和 1kg。这项工作不仅为 TAM 提供了一种可靠的自感测控制方法,也推动了仿生软机器人的发展。

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