Extensive experimental investigation for the optimization of the energy consumption of a high payload industrial robot with open research dataset

The optimization of the energy consumption of Industrial Robots (IRs) has been widely investigated. Unfortunately, on the field, the prediction and optimization strategies of IRs energy consumption still lack robustness and accuracy, due to the elevated number of parameters involved and their sensitivity to environmental working conditions. The purpose of this paper is to present, and share with the research community, an extensive experimental campaign that can be useful to validate virtual prototypes computing the energy consumption of robotic cells. The test cell, comprising a high payload IR equipped with multiple sensors and different payloads, is firstly described. The testing procedures are then presented. Experimental results are analyzed providing novel qualitative and quantitative evaluations on the contribution and relevance of different power losses and system operating conditions, clearly depicting the nonlinear relation between the energy consumption and various freely programmable parameters, thus paving the way to optimization strategies. Finally, all the experimental tests data are provided in the form of an open research dataset, along with custom Matlab scripts for plotting graphs and maps presented in this paper. These tests, which are verifiable via the shared dataset, consider the overall measured IR energy consumption (as drawn from the electric network) and highlight that, in some industrially interesting case scenarios, optimization potentials for energy savings of more than 50% are possible.