摘要
为了探索海马尾巴的解旋能力,基于生物结构的启发提出了一种基于螺线构型的气动软体致动器.区别于已有的软体致动器的弯曲运动,螺线型气动软体致动器(螺线型致动器)随着输入气压的增大可以实现正负曲率两个方向的展开运动.首先,在保持基体外侧弧长相同的情况下,依据圆形、阿基米德螺线、对数螺线3种不同螺线的数学表达式对螺线型致动器进行了3种结构设计;其次,基于超弹性材料模型、几何关系和虚功原理建立并修正了通用的静力学模型,描述输入气压与展开角度的关系;然后,对3种结构的螺线型致动器进行样机制作,并测试了它们的运动性能;最后,对比静力学模型计算结果和样机实验数据,最大平均展开角度误差为10.0166°,证明了模型的准确性.另外,根据从形状刻度线采集的坐标数据转换得到的构型空间参数,重建了螺线型致动器在不同展开状态下的理论展开形状,验证了构型空间参数获取方法的可行性.
In order to explore the unwinding ability of the seahorse tail, a pneumatic soft actuator(PSA) with the spiral configuration is proposed based on the biological inspiration. Different from the bending motion of common soft actuators,the spiral pneumatic soft actuator(spiral PSA) can realize the unwinding motion in both directions of the positive and the negative curvature as the input air pressure increases. Firstly, 3 types of structures of the spiral PSAs are designed based on the mathematical expression of 3 different spirals, the circular spiral, the Archimedes spiral, and the logarithmic spiral,keeping the length of the outside arc of the actuator bodies the same. Secondly, the general statics model of the spiral PSA is established and modified based on the superelastic material model, the geometric relationship and the virtual work principle,to describe the relationship between the input pressure and the unwinding angle. Thirdly, the 3 types of structures of the spiral PSAs are prototyped, and their actual unwinding performances are tested. Finally, the data calculated by statics model is compared with the data from prototype experiments, and the maximum average unwinding angle error is 10.0166°, which proves the accuracy of the model. In addition, the actuator theoretical unwinding shapes under different unwinding states are reconstructed according to the configuration parameters converted from experimental data of shape scale lines, and the feasibility of the method for obtaining the configuration parameters is verified.
作者
张志远
王松涛
王学谦
孟得山
梁斌
ZHANG Zhiyuan;WANG Songtao;WANG Xueqian;MENG Deshan;LIANG Bin(Shenzhen Key Laboratory of Artificial Intelligence and Robotics,Graduate School at Shenzhen,Tsinghua University,Shenzhen 518055,China;Research Institute of Tsinghua University at Shenzhen,Shenzhen 518057,China;Department of Automation,Tsinghiw University,Beijing 100084.China)
出处
《机器人》
EI
CSCD
北大核心
2020年第1期10-20,共11页
Robot
基金
国家自然科学基金(U1813216)
广东省自然科学基金(2018A030313047)
深圳市基础研究计划(JCYJ20160301100921349)。