Research on object recognition for grasping by flexible bionic manipulator based on FBG sensors and Markov transition field

Abstract To achieve accurate recognition of objects grasped by a three-fingered flexible bionic robotic gripper, this study proposes an identification method based on fiber Bragg grating (FBG) sensing and Markov image coding. Firstly, the grasping process of the manipulator was analyzed through ANSYS finite element simulation. The stress and strain distribution on the inner side of the gripper was simulated under three load conditions (3 N, 5 N, and 7 N) to determine the optimal packaging position of the FBG sensor. Subsequently, FBG sensors were packaged on the inner sides of the three fingers of the manipulator, and grasping experiments were conducted on nine objects with different diameters, materials, and shapes. For the three-channel sensing data, a Markov transition field (MTF) was used for fusion coding to convert time-series tactile signals into image features, and a strategy of using self-defined weights was proposed to construct the image dataset. Finally, the VGG11 convolutional neural network was employed for classification training and testing of the three datasets. Experimental results show that: compared with other weight coding methods, the self-designed weight method achieves an accuracy of 97.14% with an average recognition time of approximately 0.31 s per object. Moreover, the self-designed weight coding performs better in terms of loss and accuracy convergence on training and validation sets, with stable loss free of fluctuations, sustained increase in training accuracy, stable generalization, and efficient fitting. This study verifies the effectiveness and reliability of the proposed method in the recognition of objects grasped by the manipulator, and provides a feasible solution for the tactile perception and object recognition of flexible bionic manipulators.