ANSYS在腕力传感器结构设计中的应用

腕力传感器是一类重要的机器人传感器,腕力传感器的弹性体结构设计好坏直接影响到传感器的各项指标.由于腕力传感器结构复杂,各输出通道之间往往存在着干扰,通常采用实验的方法来进行标定,但标定的结果往往不够精确.利用所研制的一种新型机器人多维腕力传感器的结构,提出了一种利用有限元分析软件ANSYS对其维间干扰作定量分析的方法,分析结果证实了该传感器弹性体结构设计的合理性.     

机器人力传感器研究概况

介绍了用于机器人力控制和主动式顺应控制的几种力传感器,着重分析了几种典型的腕力传感器的弹性体结构和它们的优缺点。     

基于正交试验的应变式六维力传感器弹性体结构的优化设计

文章介绍了一种应变式六维力传感器弹性体的结构,利用SloidWorks软件建立了该传感器弹性体的有限元分析模型,采用正交试验方法全面分析了弹性体主要结构参数对弹性体灵敏度和固有频率的影响,并采用极差分析方法分析各结构参数的优水平及对指标的主次关系,得出各结构尺寸的最佳方案,从而达到了优化弹性体结构的目的,提高了传感器的动、静态特性。     

机器人腕力传感器应变片组桥方式的改进

本文对弹性体为十字梁形的六维腕传感器提出一种新的应变片组桥方式.这种方式减少了所需应变片的数量,降低了腕力传感器的生产工艺难度和生产成本,从而可以设计制造出更为小巧和可靠的腕力传感器.     

大量程六维力传感器设计与标定研究

为提高大量程六维力传感器的灵敏度,设计了一种适用于机械臂末端的应变式传感器.该传感器采用十字梁式弹性体结构,通过优化应变梁结构和合理布置应变片等方式,使传感器在大量程条件下仍具有较高的刚度和灵敏度.使用有限元法分析验证了传感器应力分布的合理性,并根据最小二乘法标定原理对传感器进行解耦标定实验,实验结果表明:传感器静态性能良好,最小灵敏度为0.375 mV/N,满足使用要求.     

串联结构的电阻应变式测力传感器

研制的串联结构应变式测力传感器是一种新型的复合结构传感器,适用于测量幅度变化大的力值。介绍其工作原理和测量系统,分析了其复合弹性体的特性并建立其动态数学模型。     

新型应变式厚膜力传感器及其应用

介绍一种可用于称重与测力的电阻应变式厚膜力传感器.其弹性体采用95%Al_2O_3陶瓷双孔梁、应变电阻为钌酸盐厚膜应变电阻,并运用厚膜工艺将应变电阻直接印刷、烧结在弹性体上而形成整体.它的量程最高达500N,精度为0.05%,工作温度达-40～ 120℃.     

机械手三维力传感器的设计

介绍了一种安装在医疗机械手腕部的三维力传感器,该传感器采用电阻应变式电桥的测量方法,其弹性体为两个扁环式结构,在两个扁环的相应敏感部位分别粘贴三组应变计,解决了三维传感器体积大、刚度低等问题;其具有量程大、体积小、精度高、横向干扰小等特点,在实际应用中收到了良好的效果.     

应变式传感器弹性体的选材及热处理

制造高精度的应变式传感器最重要的问题是弹性体的质量，而对于弹性体的质量的好坏，其原材料及热处理工艺是关键。本文对弹性体原材料的选材和三种典型代表材料的热处理进行了研究。     

DRP型应变式差压传感器及变送器的研制

本文介绍了利用凸台圆膜片与测力传感器组合而研制成的DPR型应变式差压传感器和应变式差压变送器。实践证明,在中、高差压范围内,该仪表对于双向限位保护,消除弹性梁振荡,提高应变式差压传感器的精度及耐腐蚀、耐高温等都是非常有效的。     

新型力解耦和各向同性机器人六维力传感器

介绍了基于并联 6 -SPS结构的新型机器人六维力与力矩传感器的结构特点 ,并分析计算了其力与应变之间的转换关系及其力各向同性 ,为该六维力与力矩传感器的设计和使用提供了理论依据。分析计算结果表明该传感器是力与力矩解耦和各向同性的。该传感器采用弹性铰链替代球铰 ,具有结构灵巧、工艺性好等优点 ,可应用到机器人手腕、手指和其它使用多维力与力矩传感器的场合。     

六维腕力传感器惯性参数的在线识别

腕力传感器的动态特性对机器人系统的动态特性、精确运动和控制有不可忽视的影响。将六维腕力传感器置于机器人系统中 ,基于腕力传感器坐标系内的微分运动 ,结合机器人运动学和动力学知识 ,研究了在线识别六维腕力传感器惯性参数的方法     

Six-axis force sensor evaluation and a new type of optimal frame truss design for robotic applications

The different means currently available for six-axis wrist force sensor evaluation are discussed, and a unified criteria is proposed that is based on the condition number, the overall static and dynamic stiffness of the sensor, and the strain gauge sensitivity. In this light a new frame/truss type of sensor body design is introduced. The uniqueness of the design lies in the elastic members that exhibit truss (axial deformation), as opposed to the commonly used beam (bending) behavior. Several improvements over previous designs result, including: increased force sensitivity with a consistently low condition number, increased rigidity, and improved design flexibility. In addition, a design methodology is presented that uses optimization theory in combination with finite element analysis, to yield the best possible frame/truss force sensor design for a given set of specified principal forces.     

Development and application of a multi-axis dynamometer for measuring grip force

Objective: This paper describes the development and application of a novel multi-axis hand dynamometer for quantifying 2D grip force magnitude and direction in the flexion-extension plane of the fingers. Methods: A three-beam reconfigurable form dynamometer, containing two active beams for measuring orthogonal forces and moments regardless of point of force application, was designed, fabricated and tested. Maximum grip exertions were evaluated for 16 subjects gripping cylindrical handles varying in diameter. Results: Mean grip force magnitudes were 231 N (SD = 67.7 N), 236 N (72.9 N), 208 N (72.5 N) and 158 N (45.7 N) for 3.81 cm, 5.08 cm, 6.35 cm and 7.62 cm diameter handles, respectively. Grip force direction rotated clockwise and the centre of pressure moved upward along the handle as handle diameter increased. Conclusions: Given that the multi-axis dynamometer simultaneously measures planar grip force magnitude and direction, and centre of pressure along the handle, this novel sensor design provides more grip force characteristics than current sensor designs that would improve evaluation of grip characteristics and model-driven calculations of musculoskeletal forces from dynamometer data.     

Design of a six-axis wrist force/moment sensor using FEM and its fabrication for an intelligent robot

This paper describes the design of a six-axis force/moment sensor using FEM (finite element method) and its fabrication. In order to safely grasp an unknown object using an intelligent hand in robot, the hand has to perceive the weight of it. The weight is calculated by forces F_x, F_y, F_z measured from the six-axis wrist force/moment sensor attached to an intelligent robot's hand. And, in order to accurately push and pull an object, forces and moments should be measured. Also, the position of the robot's finger contacted on an object are calculated by forces F_x, F_y and F_z, and moments M_x, M_y and M_z measured from the six-axis wrist force/moment sensor. Therefore, an intelligent robot's hand should get a six-axis wrist force/moment sensor that can measure forces F_x, F_y and F_z, and moments M_x, M_y and M_z simultaneously. The size of the six-axis force/moment sensor for an intelligent robot’ wrist is very important. If its diameter is larger or its thickness (length) is longer, it cannot be mounted in robot's wrist or it will break down under the applied moment M_x or M_y. So, its size is similar to that of the wrist of human being, that is, the diameter is about 60–80 mm and the thickness (length) about 20–40 mm. But the manufactured sensors are not proper in size for the intelligent robot's wrist. Thus, the six-axis force/moment sensor should be developed for the intelligent robot's wrist.In this paper, the structure of a six-axis wrist force/moment sensor was modeled for an intelligent hand in robot newly. And the sensing elements of it were designed by using FEM and were fabricated by attaching strain-gages on the sensing elements. And, the characteristic test of the developed sensor was carried out. The rated outputs from FEM analysis agree well with the results from the experiments. The interference error of the sensor is less than 2.85%.     

基于腕力传感器的机器人操作臂惯性参数辨识方法

针对工业机器人结构设计及动力控制中,在比现有方法少用了基座力传感器的基础上,充分挖掘了腕力传感器的信息,利用机械臂运动学及动力学递推关系,由Newton-Euler方程导出了各连杆惯性参数逆向的辨识模型．以一个算例说明了方法的运用,为机器人操作臂动力学建模提供了理论基础．     

PUMA760切割作业力与位置混合控制

本文介绍在 PUMA760 上实现了的切割作业控制系统。我们在 PUMA760 控制系统中引进了力传感器信号,加入了力控制算法,通过调整机器人手部位置,控制机器人与环境物体间的相互作用力。在 VAL 系统中加入了根据传感器信号动作的指令,实现了一种机器人力与位置的混合控制。用扩充后的 VAL 系统编程,成功地使 PUMA760完成了多种材料的切割作业。