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用于机器人快速驱动的混合柔顺肌肉骨骼系统。

Hybrid Compliant Musculoskeletal System for Fast Actuation in Robots.

作者信息

Wiersinga Pieter, Sleavin Aidan, Boom Bart, Masmeijer Thijs, Flint Spencer, Habtour Ed

机构信息

Faculty of Science and Engineering, University of Groningen, Postbus 72, 9700 AB Groningen, The Netherlands.

Department of Aeronautics & Astronautics, The University of Washington, Seattle, WA 98195, USA.

出版信息

Micromachines (Basel). 2022 Oct 20;13(10):1783. doi: 10.3390/mi13101783.

Abstract

A nature-inspired musculoskeletal system is designed and developed to examine the principle of nonlinear elastic energy storage-release for robotic applications. The musculoskeletal system architecture consists of elastically rigid segments and hyperelastic soft materials to emulate rigid-soft interactions in limbless vertebrates. The objectives are to (i) improve the energy efficiency of actuation beyond that of current pure soft actuators while (ii) producing a high range of motion similar to that of soft robots but with structural stability. This paper proposes a musculoskeletal design that takes advantage of structural segmentation to increase the system's degrees of freedom, which enhances the range of motion. Our findings show that rigid-soft interactions provide a remarkable increase in energy storage and release and, thus, an increase in the undulation speed. The energy efficiency achieved is approximately 68% for bending the musculoskeletal system from the straight configuration, compared to 2.5-30% efficiency in purely soft actuators. The hybrid compliance of the musculoskeletal system under investigation shows promise for alleviating the need for actuators at each joint in a robot.

摘要

设计并开发了一种受自然启发的肌肉骨骼系统,以研究用于机器人应用的非线性弹性能量存储-释放原理。该肌肉骨骼系统架构由弹性刚性段和超弹性软材料组成,以模拟无肢脊椎动物中的刚性-柔性相互作用。目标是:(i)在提高驱动能量效率方面超越当前的纯软驱动器,同时(ii)产生与软机器人类似的高运动范围,但具有结构稳定性。本文提出了一种利用结构分段来增加系统自由度的肌肉骨骼设计,这增强了运动范围。我们的研究结果表明,刚性-柔性相互作用显著提高了能量存储和释放,从而提高了波动速度。将肌肉骨骼系统从直线配置弯曲时,实现的能量效率约为68%,而纯软驱动器的效率为2.5%-30%。所研究的肌肉骨骼系统的混合柔顺性有望减少机器人每个关节处对驱动器的需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be28/9611504/b52386551189/micromachines-13-01783-g001.jpg

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