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Energy consumption analysis and optimization in collaborative robots
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DOI：10.3389/frobt.2025.1671336.png)
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Abstract
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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.
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