Contact State Estimation for Peg-in-Hole Assembly Using Gaussian Mixture Model

Recently, the robotic assembly has been expanded into an unstructured environment. This environment includes uncertainties that may cause unexpected situations such as a failure of the assembly. Such problems can be prevented or monitored by a robust contact state (CS) estimation method. In that sense, the paper suggests a CS estimation method that contains a torque indicator, a position/velocity indicator, and a CS discriminator. Using joint torque of manipulators and position/velocity of the end-effector, a Gaussian Mixture Model (GMM) builds each indicator by reflecting on two properties of measured data, i.e., non-stationary behavior and correlation among the data. The indicators play a role to indicate the corresponding sensor state. The discriminator is defined by rules which combine the results of the indicators, allowing a robust CS estimation to be achieved. In this respect, the proposed method has a distinct advantage over existing distance-based clustering methods which ignore probabilistic properties or correlation among measured data. The performance of the estimation is demonstrated through experiments with torque-controlled manipulators and commercial prefabricated furniture.