Service robots frequently operate various cylindrical objects with unknown physical properties, which demands the grippers of robots being equipped with force sensors to control grasp force. But force sensors are unnecessary and expensive for imprecise grasp force control for most operations in domestic environment. So as a substitute, this paper introduced the fuzzy hardness (FH) for imprecise grasp force evaluation. In addition, a method to infer the FH of objects was proposed, through vision and supervised learning. In this method, the deformation of objects related to the close degree of gripper was treated as a key variable and measured via visual methods. Based on the measured deformation data, long short-term memory network (LSTM) was introduced to conduct supervised learning synchronously. Then, several predicted deformation curves can be obtained through these LSTM blocks. Subsequently, the FH of objects would be clear when the errors between measured data and the predicted ones were calculated from the curves. The verification experiments showed that the maximum inference accuracy can reach 100% on TPU(80A) with 2 mm wall thickness. Moreover, after FH being applied, the deformation of TPU(80A) objects with 2 mm wall thickness decreased approximately 84.4% compared with using classical method. And all these results indicate that the FH inference method can be applied to adjust the grasp force for force sensor-less robots.

维修机器人经常操作各种物理特性未知的圆柱形物体，这要求机器人的抓具配备有力传感器以控制抓地力。但是对于在家庭环境中的大多数操作而言，对于不精确的抓握力控制而言，力传感器是不必要且昂贵的。因此，本文引入模糊硬度（FH）进行不精确抓握力评估。另外，提出了一种通过视觉和监督学习来推断物体的跳频的方法。在这种方法中，与抓取器紧密程度有关的物体的变形被视为关键变量，并通过视觉方法进行测量。根据测得的变形数据，引入长短期记忆网络（LSTM）同步进行监督学习。然后，通过这些LSTM块可以得到几个预测的变形曲线。随后，当从曲线计算出测量数据与预测数据之间的误差时，对象的FH将变得清晰。验证实验表明，在壁厚2 mm的TPU（80A）上，最大推理精度可以达到100％。此外，在应用FH后，与传统方法相比，壁厚2 mm的TPU（80A）物体的变形降低了约84.4％。所有这些结果表明，FH推断方法可用于调整无力传感器机器人的抓地力。验证实验表明，在壁厚2 mm的TPU（80A）上，最大推理精度可以达到100％。此外，在应用FH后，与传统方法相比，壁厚2 mm的TPU（80A）物体的变形降低了约84.4％。所有这些结果表明，FH推断方法可用于调整无力传感器机器人的抓地力。验证实验表明，在壁厚2 mm的TPU（80A）上，最大推理精度可以达到100％。此外，在应用FH后，与传统方法相比，壁厚2 mm的TPU（80A）物体的变形降低了约84.4％。所有这些结果表明，FH推断方法可用于调整无力传感器机器人的抓地力。