Li Qihang, Wang Wenpeng, Liu Joshua, Jain Amit, Armand Mehran
Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
Department of Orthopaedic Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Proc IEEE Sens. 2023;2023. doi: 10.1109/sensors56945.2023.10324929. Epub 2023 Nov 28.
We propose a novel inexpensive embedded capacitive sensor (ECS) for sensing the shape of Continuum Dexterous Manipulators (CDMs). Our approach addresses some limitations associated with the prevalent Fiber Bragg Grating (FBG) sensors, such as temperature sensitivity and high production costs. ECSs are calibrated using a vision-based system. The calibration of the ECS is performed by a recurrent neural network that uses the kinematic data collected from the vision-based system along with the uncalibrated data from ECSs. We evaluated the performance on a 3D printed prototype of a cable-driven CDM with multiple markers along its length. Using data from three ECSs along the length of the CDM, we computed the angle and position of its tip with respect to its base and compared the results to the measurements of the visual-based system. We found a 6.6% tip position error normalized to the length of the CDM. The work shows the early feasibility of using ECSs for shape sensing and feedback control of CDMs and discusses potential future improvements.
我们提出了一种用于感知连续体灵巧机械手（CDM）形状的新型廉价嵌入式电容式传感器（ECS）。我们的方法解决了与普遍使用的光纤布拉格光栅（FBG）传感器相关的一些局限性，例如温度敏感性和高生产成本。ECS 使用基于视觉的系统进行校准。ECS 的校准由一个递归神经网络执行，该网络使用从基于视觉的系统收集的运动学数据以及来自 ECS 的未校准数据。我们在一个沿其长度带有多个标记的电缆驱动 CDM 的 3D 打印原型上评估了性能。使用沿 CDM 长度的三个 ECS 的数据，我们计算了其末端相对于基座的角度和位置，并将结果与基于视觉的系统的测量值进行比较。我们发现相对于 CDM 的长度，末端位置误差为 6.6%。这项工作展示了使用 ECS 进行 CDM 形状感知和反馈控制的早期可行性，并讨论了未来可能的改进。
