Department of Electronics Engineering, Pusan National University, Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea.
Department of Electronics Engineering, Pukyung National University, Daeyeon 3-dong, Nam-gu, Busan 608-737, Korea.
Sensors (Basel). 2020 Nov 9;20(21):6390. doi: 10.3390/s20216390.
A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object.
提出了一种新的触觉传感模块，用于感测机器人手上的物体的接触力和位置，该模块安装在机器人手指上。在手指的尖端安装了三个气压传感器，以检测各点的接触力。为了从三个气压传感器的数据中获得手指上的名义接触力，基于深度学习神经网络的学习，开发了一种力估计方法。利用三个气压传感器的数据作为输入来估计手指上的接触力。在触觉模块中，利用气压传感器数据的到达时间来识别机器人手指与物体的接触点。使用三个气压传感器和到达时间，可以将手指位置分为 3×3 个块位置。使用人工神经网络，接触点识别的分辨率提高到手指上的 6×4 个块位置。通过实际的抓取实验验证了触觉模块的准确性和有效性。通过这种稳定的抓取，使用模糊规则对给定的物体进行了经验估算，得到了最佳的抓取力。
