Hardness recognition of fruits and vegetables based on tactile array information of manipulator

Abstract Hardness is an important physical property of fruits and vegetables that directly affects the effectiveness of the picking manipulators of robots. Therefore, a method for recognizing the hardness of fruits and vegetables based on tactile array information is proposed. This allows the picking manipulator to recognize the hardness properties of fruits and vegetables, thus aiding the robot to stably grasp these products without damage during picking. First, an experimental platform for the tactile information acquisition system of the manipulator was built to collect the original dataset of the tactile sequence generated as the manipulator grasps the fruits and vegetables in real time. Then, two classification models for recognizing the hardness of fruits and vegetables were formulated and tested. In these two proposed models, the sample feature set obtained after the dimensionality reduction processing by the principal component analysis (PCA) was used to train and test the two classifiers based on k-nearest neighbor (KNN) and support vector machine (SVM) algorithm. The classification accuracy rates of the PCA–KNN and PCA–SVM are 90.03% and 94.27%, respectively, indicating that the accuracy of the latter is significantly better than that of the former. Finally, an online grabbing recognition experiment using the manipulator was implemented to verify the practicability of the PCA–SVM classifier. The accuracy rate of online recognition reached 90%, which is a noteworthy experimental result.