FPGA-Based Implementation of Stochastic Configuration Network for Robotic Grasping Recognition

Precise and rapid grasping recognition based on machine vision is one of the challenging problems for intelligent robots. As a nonlinear network for recognition, the stochastic configuration network (SCN) is considered as a promising method due to its universal approximation capability and fast modelling property, therefore, in this paper, SCN with depth image is applied to robotic grasping recognition for the first time. Moreover, in order to obtain better grasping performance, a novel autoencoder-based SCN (SCN-AE) is proposed to extract the higher level features of the captured image. Finally, a field programmable gate array (FPGA) framework for implementing SCN models is designed and analyzed, which can effectively balance the recognition accuracy and resource utilization. Experimental results on Cornell grasping dataset proved that the proposed SCN-AE method achieved higher accuracy and better stability in robotic grasping recognition task, and the designed FPGA framework implemented the SCN and SCN-AE recognition models successfully. The average recognition accuracy of the proposed SCN-AE based on FPGA reaches 91.935%.