Tactile sensing is an essential perception for robots to complete dexterous tasks. As a promising tactile sensing technique, vision-based tactile sensors have been developed to improve robot performance in manipulation and grasping. Here we propose a new design of a vision-based tactile sensor, DelTact. The sensor uses a modular hardware architecture for compactness whilst maintaining a contact measurement of full resolution (798*586) and large area (675mm2). Moreover, it adopts an improved dense random color pattern based on the previous version to achieve high accuracy of contact deformation tracking. In particular, we optimize the color pattern generation process and select the appropriate pattern for coordinating with a dense optical flow algorithm under a real-world experimental sensory setting. The optical flow obtained from the raw image is processed to determine shape and force distribution on the contact surface. We also demonstrate the method to extract contact shape and force distribution from the raw images. Experimental results demonstrate that the sensor is capable of providing tactile measurements with low error and high frequency (40Hz).
翻译:触觉感测是机器人完成感官任务的基本概念。 作为一种有希望的触觉感测技术, 已经开发了视觉触觉感应器, 以提高机器人在操作和捕捉方面的性能。 我们在这里提出了基于视觉的触觉感应器DelTact的新设计。 感应器使用一个模块硬件结构, 并保持全分辨率( 798*586) 和大面积( 675mm2) 的触觉测量, 同时保持全分辨率( 798*586) 和大面积( 675mm2) 的接触度测量。 此外, 它采用了基于先前版本的改良的稠密随机色谱模式, 以达到接触变异性跟踪的高度精确性。 特别是, 我们优化了颜色模式生成过程, 并选择了与现实世界实验感官环境下的密集光学流算法相协调的适当模式。 从原始图像获得的光学流经过处理, 以确定接触表面的形状和分布。 我们还演示了从原始图像中提取接触形状和力分布的方法。 实验结果显示传感器能够以低误和高频度( 40Hz) 提供触觉测量测量。 。 。 。 。