Contact State Classification in Industrial Robotic Assembly Tasks Based on Extreme Learning Machine

Abstract

In industrial robotic assembly process, the work surrounding environment is generally described by Contact State (CS). In order to solve the issue of contact state recognition, contact state classification based upon Extreme Learning Machine (ELM) is proposed in industrial robotic assembly process and extended the neural network to kernel learning in this paper. ELM has excellent performance in accuracy and speed of classification, owing to node generation being independent of training data, and ELM with kernel (ELM-kernel), based on the basic classifier ELM, is applied to classify contact state of the complex assembly process. Results have shown that contact state can be recognized by the proposed classification method and the classification performance of ELM-kernel is superior to ELM. In this way, more accurate information of contact states can be provided for the robot favoring the assembly tasks.