Abstract—This paper established power consumption modeling and motion estimation optimization of industrial robots. We also studied factors affecting the use of electrical energy, such as friction, torque, and electric current. The energy consumption parameters of each coupling can be quantified through the Deep Learning (DL) technique, Scaled Conjugate Gradient (SCG) estimation, or Simulation and experimentation based on the movement posture of a given robot dynamic model to control the robot operation. The robot dynamic model parameters can be identified and expressed in mathematical equations. Electrical energy consumption estimates were analyzed using the SCG technique to compare with the Nonlinear Least Squares (NLS) method using a large dataset of approximately 60,000 samples. The results showed accurate parameter prediction and electrical energy consumption estimation of the robot locomotion pose. The maximum errors in the SCG and NLS methods were 0.89% and 1.54%, respectively. It indicated that the electric energy consumption model using the SCG estimation method is more efficient than the NLS method.

Keywords—robot power consumption, Deep Learning (DL), Nonlinear Least Squares (NLS), Scaled Conjugate Gradient (SCG)

Cite: Borihan Butsanlee, Watcharin Pongaen, Nuttapon Rothong, Supawan Ponpitakchai, and Songkran U-Thathong, "Development of Deep Learning for Power Energy Optimization in the Industrial Robot System," International Journal of Mechanical Engineering and Robotics Research, Vol. 13, No. 2, pp. 254-265, 2024.

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