A hidden Markov model-based assembly contact recognition system

Abstract

A hidden Markov model (HMM)-based assembly contact state recognition system is designed and implemented. The system utilizes the force/torque data captured from a wrist force sensor to extract the intrinsic spatial relationships of contact formations arise from robotic assembly. The paper introduces a theoretical framework for contact recognition based on discrete HMM with special emphasis on the practical realization of the system using an industrial robot. With the detailed exposition of the major algorithms for solving the three key problems in HMM, namely, the evaluation of observation sequence probability, optimization of state sequence probability, and optimization of model parameters, a working prototype of HMM-based system is developed. The performance of the contact recognition system is investigated by using experimental studies. The results obtained clearly demonstrate that HMM is an effective means for assembly contact modelling and identification, and that the framework reported in this paper provides an essential ground work for the development of a practical intelligent robotic system.