Evaluation of two complementary modeling approaches for fiber-reinforced soft actuators

IF 1.5 Q3 INSTRUMENTS & INSTRUMENTATION
Habibian, Soheil, Wheatley, Benjamin B., Bae, Suehye, Shin, Joon, Buffinton, Keith W.
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引用次数: 2

Abstract

Although robots are increasingly found in a wide range of applications, their use in proximity to humans is still fraught with challenges, primarily due to safety concerns. Roboticists have been seeking to address this situation in recent years through the use of soft robots. Unfortunately, identifying appropriate models for the complete analysis and investigation of soft robots for design and control purposes can be problematic. This paper seeks to address this challenge by proposing two complementary modeling techniques for a particular type of soft robotic actuator known as a Fiber-Reinforced Elastomeric Enclosure (FREE). We propose that researchers can leverage multiple models to fill gaps in the understanding of the behavior of soft robots. We present and evaluate both a dynamic, lumped-parameter model and a finite element model to extend understanding of the practicability of FREEs in soft robotic applications. The results of experimental simulations using a lumped-parameter model show that at low pressures FREE winding angle and radius change no more than $$2\%$$ . This observation provided confidence that a linearized, dynamic, lumped-mass model could be successfully used for FREE controller development. Results with the lumped-parameter model demonstrate that it predicts the actual rotational motion of a FREE with at most $$4\%$$ error when a closed-loop controller is embedded in the system. Additionally, finite element analysis was used to study FREE design parameters as well as the workspace achieved with a module comprised of multiple FREEs. Our finite element results indicate that variations in the material properties of the elastic enclosure of a FREE are more significant than variations in fiber properties (primarily because the fibers are essentially inextensible in comparison to the elastic enclosure). Our finite element analysis confirms the results obtained by previous researchers for the impact of variations in winding angle on FREE rotation, and we extend these results to include an analysis of the effect of winding angle on FREE force and moment generation. Finally, finite element results show that a $$30^{\circ }$$ difference in winding angle dramatically alters the shape of the workspace generated by four FREEs assembled into a module. Concludingly, comments are made about the relative advantages and limitations of lumped-parameter and finite element models of FREEs and FREE modules in providing useful insights into their behavior.
纤维增强软执行器两种互补建模方法的评价
尽管机器人在越来越广泛的应用中被发现,但它们在接近人类时的使用仍然充满挑战,主要是由于安全问题。近年来,机器人专家一直在寻求通过使用软机器人来解决这一问题。不幸的是,为软机器人的设计和控制目的的完整分析和调查确定适当的模型可能是有问题的。本文试图通过提出两种互补的建模技术来解决这一挑战,这种建模技术被称为纤维增强弹性体外壳(FREE)。我们建议研究人员可以利用多个模型来填补对软机器人行为理解的空白。我们提出并评估了一个动态的集总参数模型和一个有限元模型,以扩展对自由自由度在软机器人应用中的实用性的理解。采用集总参数模型的实验模拟结果表明,在低压下,自由缠绕角和半径的变化不超过$$2\%$$。这一观察结果为线性化、动态、集中质量模型可以成功地用于FREE控制器的开发提供了信心。集总参数模型的结果表明,当系统中嵌入闭环控制器时,集总参数模型预测自由度的实际旋转运动误差不超过$$4\%$$。此外,采用有限元分析方法研究了由多个自由度组成的模块的自由度设计参数和工作空间。我们的有限元结果表明,FREE弹性外壳材料性能的变化比纤维性能的变化更显著(主要是因为与弹性外壳相比,纤维本质上是不可扩展的)。我们的有限元分析证实了先前研究人员关于缠绕角变化对自由旋转的影响的结果,并扩展了这些结果,包括缠绕角对自由力和力矩产生的影响的分析。最后,有限元结果表明,$$30^{\circ }$$缠绕角的差异会显著改变由四个自由体组装成一个模块所产生的工作空间形状。最后,对自由模组和自由模组的集总参数模型和有限元模型的相对优势和局限性进行了评论,从而对自由模组和自由模组的行为提供了有用的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ROBOMECH Journal
ROBOMECH Journal Mathematics-Control and Optimization
CiteScore
3.20
自引率
7.10%
发文量
21
审稿时长
13 weeks
期刊介绍: ROBOMECH Journal focuses on advanced technologies and practical applications in the field of Robotics and Mechatronics. This field is driven by the steadily growing research, development and consumer demand for robots and systems. Advanced robots have been working in medical and hazardous environments, such as space and the deep sea as well as in the manufacturing environment. The scope of the journal includes but is not limited to: 1. Modeling and design 2. System integration 3. Actuators and sensors 4. Intelligent control 5. Artificial intelligence 6. Machine learning 7. Robotics 8. Manufacturing 9. Motion control 10. Vibration and noise control 11. Micro/nano devices and optoelectronics systems 12. Automotive systems 13. Applications for extreme and/or hazardous environments 14. Other applications
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