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.