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 sen...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2022-04, Vol.7 (2), p.3349-3356
Main Authors: Lee, Haeseong, Park, Suhan, Jang, Keunwoo, Kim, Seungyeon, Park, Jaeheung
Format: Article
Language:English
Subjects:
Assembly
Clustering
contact modeling
End effectors
Force
Indicators
Manipulators
perception for grasping and manipulation
Position indicators
Prefabrication
Probabilistic models
Robustness
State estimation
Task analysis
Torque
Torque measurement
Trajectory
Uncertainty
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3146949