Classification of Contact Forces in Human-Robot Collaborative
Manufacturing Environments 05-11-01-0001
This also appears in
SAE International Journal of Materials and Manufacturing-V127-5EJ
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.
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. Download Citation
Author(s):
Ran Zhao, Svetan Ratchev, Adrien Drouot
Affiliated:
University of Nottingham
Pages: 6
ISSN:
1946-3979
e-ISSN:
1946-3987
Related Topics:
Robotics
Machine learning
Assembling
Sensors and actuators
Parts
Railway vehicles and equipment
Doors
Seals and gaskets
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