Modeling Contact State of Industrial Robotic Assembly Using Support Vector Regression

Abstract

In industrial robotic assembly process, the work surrounding environment is generally described by contact state when vision-based systems fail for occluded parts. To solve contact state recognition problem, the paper builds an assembly process model based upon Support Vector Regression (SVR) and Particle Swarm Optimal (PSO), which maps the relationship between assembly contact state and robotic executive action. The established SVR model, whose parameters is optimized by PSO, used to predict the next motion of robot. The effectiveness and accuracy of the established model based on SVR and PSO are further demonstrated by experiments using fasten-assembly of circuit breaker in low-voltage apparatus automotive assembly. Results show that the proposed method is able to model the complex assembly process of low-voltage apparatus to construct the assembly rule base and lay the foundation for improving the flexibility and rapidity of small assembly.