In-Hand Small-Object Counting from Tactile Sensor Arrays Installed on Soft Fingertips
In-Hand Small-Object Counting from Tactile Sensor Arrays Installed on Soft Fingertips
复制标题
通过安装在柔软指尖上的触觉传感器阵列进行手持小物体计数
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Y. Kawahara
中科院分区：
文献类型：
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作者：
Matthew Ishige;T. Umedachi;Yoshihisa Ijiri;Y. Kawahara
在物体拾取过程中，了解被拾取物体的状态对于保证后续任务的顺利进行是非常重要的。特别是，计数手中物体的能力对于判断前一次拾取动作是否成功至关重要。这项工作旨在赋予这种能力的机器人操作。由于遮挡问题，基于视觉的方法不能依赖于计数手中的物体，特别是在处理小于一厘米的物体时，如小螺钉。因此，应该根据触觉传感器信息进行数量估计。然而，紧凑的基于压力的触觉传感器阵列不能获得这样小的物体的精细轮廓，因为传感器元件尺寸不够小，这意味着简单的基于规则的方法是不可行的。此外，固定在刚性平面表面上的触觉传感器阵列只能接触手持物体的突出部分;因此，简单地将触觉传感器阵列安装在操纵器表面上不足以对物体进行计数。因此，在这项工作中，我们提出了1）一个数字估计方法，它使用卷积神经网络和2）覆盖触觉传感器阵列与软材料，以丰富触觉信息，以提高估计精度。我们验证了所提出的方法，使用一个简单的夹具收集的数据，并达到89%的精度估计在夹具中的小螺丝数量。
In object picking, knowing the state of picked up objects is very important to conduct succeeding tasks surely. Especially, the ability to count objects in hand is crucial to judge whether the previous picking action was successful or not. This work seeks to endow such ability to a robot manipulator. Vision-based methods cannot be relied on to count objects in hand due to the occlusion problem, especially when dealing with objects smaller than one centimeter like small screws. Hence, number estimation should be conducted from tactile sensor information. However, compact pressure-based tactile sensor arrays can not take fine outlines of such small objects because sensor element size is not small enough, meaning that a simple rule-based approach is not feasible. Furthermore, the tactile sensor array fixed on a rigid plane surface can only contact protruding parts of in-hand objects; thus, the simple installation of tactile sensor arrays on a manipulator surface is insufficient for counting objects. Therefore, in this work, we propose 1) a number estimation method which uses a convolution neural network and 2) to cover a tactile sensor array with soft material to enrich tactile information to improve estimation accuracy. We validated the proposed method using data collected by a simple gripper and achieved 89% accuracy in estimating the small screw number in the gripper.