用于缺陷检测的指尖型大面积光学式触觉传感器设计与性能研究

Design and performance research of a large-area fingertip optical tactile sensor for surface defect detection

对各种具有复杂曲面及平面的材料进行缺陷检测的过程中,传统触觉传感器存在探测面积小、探测效率低等缺点。针对这些问题设计并制备了一种指尖型大面积光学式触觉传感器,并将该传感器应用于多种材料的表面缺陷探测中。所设计的传感器外形类似人类手指尖端,同时具备指型曲面和平面双接触面,可满足各种复杂接触面的探测需求。传感器中设计了微型传动装置用于带动摄像头转动以提高成像质量,并通过转动采集多张图像使用APAP图像拼接算法对其进行拼接,增大单次探测有效面积。通过模拟多种材料表面缺陷并制作触觉图像数据集,采用DeepLabv3模型对其进行训练。实验结果表明,在单次采集的情况下,有效探测面积达到16.3 cm^(2),模型通过训练MIoU达到91.2%,可实现多种材料复杂曲面和平面的缺陷探测。

In the process of defect detection for various materials with complex curved surfaces or flat surfaces,conventional tactile sensors have disadvantages,such as small detection area and low detection efficiency.To address these problems,a fingertip type large-area optical tactile sensor is designed and prepared for multi-material surface defect detection.It is similar to the tip of human finger and has both finger-shaped curved and flat contact surfaces,which can meet the detection needs of various complex contact surfaces.A miniature actuator is designed in the sensor to drive the camera rotation to improve the imaging quality,and multiple images are collected by rotation and stitched together using the APAP image stitching algorithm to increase the effective area for single detection.A variety of material surface defects are simulated and a tactile image dataset is created,which is trained by the DeepLabv3 model.Experimental results show that,with a single acquisition,the effective detection area reaches 16.3 cm^(2),and the model achieves 91.2%MIoU through training,which enables the detection of defects on complex surfaces and planes of multiple materials.