Online Measuring of Robot Positions Using Inertial Measurement Units, Sensor Fusion and Artificial Intelligence

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.

Automated summary

This research introduces a new method to estimate the position of a robot's Tool Center Point (TCP) using IMUs, sensor fusion, and ANNs. The method employs a non-parametric approach to reset the reference dynamically, minimize sensor noise, and accurately predict the TCP position in real-time.