Abstract
With strong reliability and maneuverability, industrial robots play an essential role in the transformation and upgrading of the manufacturing industry, known as industry 4.0. Due to their mass application, it is essential to optimize the energy consumption of industrial robots considering the rising cost of energy resources and deteriorating eco-environment. State of the art solutions have mainly focused on hardware improvement and trajectory-planning-oriented software approaches, implementation of which is, nevertheless, restricted by the commercial feature of control algorithms of industrial robots. In order to explore the potential on energy saving, a data-driven approach for practical energy consumption optimization is proposed in this paper. Different from traditional energy consumption modelling where kinematic and dynamic behaviors of industrial robots are the major concern, this study use an artificial neural network to accurately reveal the quantitative relations between operating parameters and energy consumption, then genetic algorithm to optimize the adjustable parameters to minimize the consumed energy. Experiments are designed and performed on an Epson C4 6-DoF industrial robot. The results demonstrate the applicability and effectiveness of the proposed method in assisting the operating parameter selection and energy saving.
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