David G. Black;Amir Hossein Hadi Hosseinabadi;Nicholas Rangga Pradnyawira;Mika Nogami;Septimu E. Salcudean
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The modeling, simulation, and optimization of the force sensor are described and then used in the electrical and mechanical design and integration of the sensor into an ultrasound probe. Through a neural network-based nonlinear calibration, the sensor achieves average root-mean-square test errors of 0.41 N and 0.027 Nm compared to an off-the-shelf ATI Nano25 sensor, which are 0.80% and 1.16% of the full-scale range respectively. The sensor has an average noise power spectral density of less than 0.0001 N/\n<inline-formula> <tex-math>$\\sqrt {\\text {Hz}}$ </tex-math></inline-formula>\n, and a 95% confidence interval resolution of 0.0086 N and 0.063 Nmm. The practical readout rate is 1.3 kHz over USB serial and it can also operate over Bluetooth or Wi-Fi. 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引用次数: 0
摘要
手持工具上的力/力矩传感可实现对作用力的控制,这在远程机器人技术和人员远程引导中通常是必不可少的。然而,现有的力传感器要么体积庞大、结构复杂,要么额定负载不足。本文介绍了一种新型 6 轴力-力矩传感器,该传感器基于放置在工具或设备周围的一系列扁平传感器模块中的差分磁场读数。该仪器安装简单,但性能良好。此外,还介绍了详细的数学模型和基于优化的设计程序。介绍了力传感器的建模、仿真和优化,然后将其用于电气和机械设计,并集成到超声探头中。通过基于神经网络的非线性校准,与现成的 ATI Nano25 传感器相比,该传感器的平均均方根测试误差分别为 0.41 N 和 0.027 Nm,分别为满量程的 0.80% 和 1.16%。该传感器的平均噪声功率谱密度小于 0.0001 N/ $\sqrt {text {Hz}}$,95% 置信区间分辨率为 0.0086 N 和 0.063 Nmm。通过 USB 串行端口的实际读出速率为 1.3 kHz,也可通过蓝牙或 Wi-Fi 运行。该传感器可实现手动工具的仪器化,从而提高远程操作系统或自主系统的性能和透明度。
Low-Profile 6-Axis Differential Magnetic Force/Torque Sensing
Force/torque sensing on hand-held tools enables control of applied forces, which is often essential in both tele-robotics and remote guidance of people. However, existing force sensors are either bulky, complex, or have insufficient load rating. This paper presents a novel 6 axis force-torque sensor based on differential magnetic field readings in a collection of low-profile sensor modules placed around a tool or device. The instrumentation is easy to install but nonetheless achieves good performance. A detailed mathematical model and optimization-based design procedure are also introduced. The modeling, simulation, and optimization of the force sensor are described and then used in the electrical and mechanical design and integration of the sensor into an ultrasound probe. Through a neural network-based nonlinear calibration, the sensor achieves average root-mean-square test errors of 0.41 N and 0.027 Nm compared to an off-the-shelf ATI Nano25 sensor, which are 0.80% and 1.16% of the full-scale range respectively. The sensor has an average noise power spectral density of less than 0.0001 N/
$\sqrt {\text {Hz}}$
, and a 95% confidence interval resolution of 0.0086 N and 0.063 Nmm. The practical readout rate is 1.3 kHz over USB serial and it can also operate over Bluetooth or Wi-Fi. This sensor can enable instrumentation of manual tools to improve the performance and transparency of teleoperated or autonomous systems.