Regression-Based Modeling for Energy Demand Prediction in a Prototype Retail Manipulator

The present study proposes two regression-based models for predicting the energy consumption of a four-axis prototype retail manipulator. These models are developed using experimental current and voltage measurements. The Total Energy Model (TEM) is a method of estimating energy per trajectory that utilizes global motion parameters. In contrast, the Power-to-Energy Model (PEM) is a technique that reconstructs energy from predicted instantaneous power. It has been demonstrated that both models demonstrate high levels of predictive accuracy, with mean absolute percentage error (MAPE) values ranging from 1 to 1.5%. These models are well-suited for implementation in hardware-constrained environments and for integration into digital twins.