Abstract
Energy consumption is a key concern in modern industrial facilities. Power peak is also a relevant feature in industrial energy analysis and managment, since the electrical infrastructure must be implemented to provide not only the total consumed energy, but the power peaks. Collaborative robots are gaining popularity due to its flexible use and convenient set up. In this context, a power and energy consumption study of the popular UR10 collaborative robot of Universal Robots is reported in this work. For this, an experiment was conducted to obtain current consumption data from the UR10 API, when performing movements with different loads and parameters. Next, the dependency of the trajectory programming parameters on the power peak, total consumed energy, and time spent per trajectory was analyzed. The results show that the higher the speed limit and acceleration limit, the lower the total energy consumed per trajectory, but the higher the power peak. This behavior represents a trade-off: reducing the consumed energy involves increasing the peak power. Based on the captured data, artificial neural network models were trained to predict the power peak and the total energy consumed by the robot when performing a movement under certain parameters. These models were later used by a genetic optimization algorithm to obtain the best parameters for a given target position, providing the most efficient performance while fulfilling a power peak bound.