Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.

中文翻译：

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基于更快的基于区域的卷积神经网络和加工学习算法的软抓手的视觉-触觉对象识别

物体识别是控制软爪成功抓取未知物体的先决条件。视觉和触觉识别是抓取系统中两种常用的方法。如果涉及物体的大小和重量，视觉识别是有限的，而触觉识别的效率是一个问题。本文提出了一种视觉-触觉识别方法来克服这两种方法的缺点。实现了考虑视觉和触觉传感器的软抓手的设计和制造，其中采用 Kinect v2 获取视觉信息，弯曲和压力传感器嵌入软手指以获取触觉信息。该方法分为三个步骤：视觉初始识别、触摸细节识别和数据融合决策。实验表明，视觉-触觉识别效果最好。所提出的方法对日常物体的平均识别准确率也是最高的。验证了视觉触觉识别的可行性。