Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.
Bristol Robotics Laboratory, Bristol, United Kingdom.
Soft Robot. 2021 Oct;8(5):594-610. doi: 10.1089/soro.2020.0019. Epub 2020 Dec 18.
Bringing tactile sensation to robotic hands will allow for more effective grasping, along with a wide range of benefits of human-like touch. Here, we present a three-dimensional-printed, three-fingered tactile robot hand comprising an OpenHand ModelO customized to house a TacTip soft biomimetic tactile sensor in the distal phalanx of each finger. We expect that combining the grasping capabilities of this underactuated hand with sophisticated tactile sensing will result in an effective platform for robot hand research-the Tactile Model O (T-MO). The design uses three JeVois machine vision systems, with each comprising a miniature camera in the tactile fingertip with a processing module in the base of the hand. To evaluate the capabilities of the T-MO, we benchmark its grasping performance by using the Gripper Assessment Benchmark on the Yale-CMU-Berkeley object set. Tactile sensing capabilities are evaluated by performing tactile object classification on 26 objects and predicting whether a grasp will successfully lift each object. Results are consistent with the state of the art, taking advantage of advances in deep learning applied to tactile image outputs. Overall, this work demonstrates that the T-MO is an effective platform for robot hand research and we expect it to open up a range of applications in autonomous object handling.
为机器人手带来触觉将允许更有效的抓取,以及类似于人类触摸的广泛好处。在这里,我们展示了一种由 OpenHand ModelO 定制的三维打印的三指触觉机器人手,该模型可以在每个手指的远节指骨中容纳一个 TacTip 软仿生触觉传感器。我们预计,将这种欠驱动手的抓取能力与复杂的触觉传感相结合,将为机器人手研究提供一个有效的平台——触觉模型 O(T-MO)。该设计使用了三个 JeVois 机器视觉系统,每个系统都在触觉指尖上使用一个微型相机,在手头的底部使用一个处理模块。为了评估 T-MO 的能力,我们使用 Yale-CMU-Berkeley 对象集上的 Gripper Assessment Benchmark 基准测试来评估其抓取性能。通过对 26 个物体进行触觉物体分类,并预测抓取是否会成功提起每个物体,来评估触觉传感能力。结果与最新技术一致,利用了应用于触觉图像输出的深度学习的进步。总的来说,这项工作表明 T-MO 是机器人手研究的有效平台,我们预计它将在自主物体处理中开辟一系列应用。