This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Classification of Contact Forces in Human-Robot Collaborative Manufacturing Environments
ISSN: 1946-3979, e-ISSN: 1946-3987
Published April 02, 2018 by SAE International in United States
Citation: Zhao, R., Ratchev, S., and Drouot, A., "Classification of Contact Forces in Human-Robot Collaborative Manufacturing Environments," SAE Int. J. Mater. Manf. 11(1):5-10, 2018, https://doi.org/10.4271/05-11-01-0001.
This paper presents a machine learning application of the force/torque sensor in a human-robot collaborative manufacturing scenario. The purpose is to simplify the programming for physical interactions between the human operators and industrial robots in a hybrid manufacturing cell which combines several robotic applications, such as parts manipulation, assembly, sealing and painting, etc. A multiclass classifier using Light Gradient Boosting Machine (LightGBM) is first introduced in a robotic application for discriminating five different contact states w.r.t. the force/torque data. A systematic approach to train machine-learning based classifiers is presented, thus opens a door for enabling LightGBM with robotic data process. The total task time is reduced largely because force transitions can be detected on-the-fly. Experiments on an ABB force sensor and an industrial robot demonstrate the feasibility of the proposed method.