Abstract
Robot calibration is crucial in multi-robot cooperative systems where the inaccuracy of robots can add up and cause large errors in the final trajectory of handled parts or process tools. In this work, a two-step calibration approach is proposed based on artificial neural networks (ANNs) and definition of compensated pose for a master–slave cooperative robot system. Measuring the pose of master and slave robots at different locations in their shared workspace is required to create pairs of joint angles and output pose errors as training data. The generated data is used to train two ANN models for compensating the master–slave relative error and the master robot errors. The master–slave relative error is corrected by introducing a compensated pose for the slave robot with respect to the master robot. A neural network is then trained to predict the error parameters of the compensated pose for the joint angles of both robots as the input. The master robot is then corrected individually using another ANN model to address the absolute accuracy of the cooperative system. Measurements and simulations have been performed on a dual-robot cooperative system before and after geometric calibration. The process of cross validation is carried out to find the best network architecture for the optimal performance in correcting the robots’ errors. It has been shown that even after pre-existing model-based calibration of each robot, both the absolute accuracy of the master robot and the relative tracking accuracy can be further improved by the proposed implementation of ANN calibration.
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Funding
This research was supported financially by the Natural Sciences and Engineering Research Council of Canada (NSERC) under the Canadian Network for Research and Innovation in Machining Technology (CANRIMT), grant number NETGP 479639–15. Experimental platforms and material for testing were provided by the National Research Council of Canada (NRC) Aerospace Manufacturing Technologies Centre (AMTC), Montreal. The first author received funding from MITACS Canada.
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Bruno Monsarrat, Lionel Birglen, and Matt Khoshdarregi conceived, designed and lead the study. Material preparation, data collection, technical development, and analysis were performed by Ali Maghami, Alaïs Imbert, and Gabriel Côté. The first draft of the manuscript was written by Ali Maghami and all authors commented on previous versions of the manuscript.
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Maghami, A., Imbert, A., Côté, G. et al. Calibration of Multi-Robot Cooperative Systems Using Deep Neural Networks. J Intell Robot Syst 107, 55 (2023). https://doi.org/10.1007/s10846-023-01867-6
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DOI: https://doi.org/10.1007/s10846-023-01867-6