Ye Jinhua, Lin Zhengkang, You Jinyan, Huang Shuheng, Wu Haibin
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China.
Micromachines (Basel). 2020 Feb 3;11(2):162. doi: 10.3390/mi11020162.
In the field of safety and communication of human-robot interaction (HRI), using large-scale electronic skin will be the tendency in the future. The force-sensitive piezoresistive material is the key for piezoresistive electronic skin. In this paper, a non-array large scale piezoresistive tactile sensor and its corresponding calibration methods were presented. Because of the creep inconsistency of large scale piezoresistive material, a creep tracking compensation method based on K-means clustering and fuzzy pattern recognition was proposed to improve the detection accuracy. With the compensated data, the inconsistency and nonlinearity of the sensor was calibrated. The calibration process was divided into two parts. The hierarchical clustering algorithm was utilized firstly to classify and fuse piezoresistive property of different regions over the whole sensor. Then, combining the position information, the force detection model was constructed by Back-Propagation (BP) neural network. At last, a novel flexible tactile sensor for detecting contact position and force was designed as an example and tested after being calibrated. The experimental results showed that the calibration methods proposed were effective in detecting force, and the detection accuracy was improved.
在人机交互（HRI）的安全与通信领域，使用大规模电子皮肤将是未来的发展趋势。力敏压阻材料是压阻式电子皮肤的关键。本文提出了一种非阵列式大规模压阻触觉传感器及其相应的校准方法。由于大规模压阻材料的蠕变不一致性，提出了一种基于K均值聚类和模糊模式识别的蠕变跟踪补偿方法来提高检测精度。利用补偿后的数据，对传感器的不一致性和非线性进行了校准。校准过程分为两部分。首先利用层次聚类算法对整个传感器不同区域的压阻特性进行分类和融合。然后，结合位置信息，通过反向传播（BP）神经网络构建力检测模型。最后，以一种新型的用于检测接触位置和力的柔性触觉传感器为例进行设计，并在校准后进行测试。实验结果表明，所提出的校准方法在力检测方面是有效的，并且提高了检测精度。
