Abstract
This research introduces a new method to estimate the position of a robot’s Tool Center Point (TCP) using Inertial Measurement Units (IMUs), sensor fusion and Artificial Neural Networks (ANNs). The objective is to make an accurate estimate of TCP navigation, using the signals from an IMU as resources of a neural network capable of predicting the position. Considering that the IMU sensors suffer noise in the measurements and the noise progresses over time, this proposal employs a technique that eliminates the filtering step, and the process is done internally by the network. The work employs a non-parametric approach to reset the reference dynamically, minimize noise from sensors, and converge positioning to a nominal result. This method offers a solution for fast, cheap, and efficient robot calibration. The work does not want to replace current techniques but to introduce a new design to the literature. The concept does not require sophisticated mechanical parts and the production line to be idle during the calibration process, and the results show that the developed technique can accurately predict the TCP position with millimeter errors and in real-time. The study also implemented the concept with other neural networks, for which it used a smaller set of data in an attempt to reduce training time. The research used the Multilayer Perceptron and XGBRegressor networks to test the approach introduced with others algorithms. Different applications that need real-time positioning can benefit from the proposal.
Highlights
Robots today play a central role in the manufacturing industry [1], [2], and for these machines to work correctly it is necessary to perform their calibration
THE Artificial Neural Networks (ANNs)-RB ALGORITHM The network input is the 19 features provided by the Inertial Measurement Units (IMUs), and the output is a prediction of the Tool Center Point (TCP) position provided by the algorithm
This demonstrates that the ANN-RB had not yet learned to correct the values from the errors of the IMU, and it would still be necessary to make changes to the hyperparameters of the neural network and its topology
Summary
Robots today play a central role in the manufacturing industry [1], [2], and for these machines to work correctly it is necessary to perform their calibration. Robot calibration is a systemic process of modeling, measuring, numerically identifying its physical characteristics and implementing a new model [3]–[5]. Kinematic calibration is a way to improve the positioning accuracy of the robot [6]. In the case of industrial robots, it is a method of minimizing the effects of various sources of errors that affect the position and orientation (pose) accuracy of the robot TCP, due to geometric deviations and other sources of errors during its operation [7]. In the process of robot calibration, the robot’s TCP poses are measured [8] and the deviations between the desired poses in the robot program and those reached in its operation are recorded.
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