ANN Robot Energy Modeling

摘要

This paper proposes energy modeling for robot based on real measurements data. First, the paper proposes six preliminary ANN Models on both carpet and hard floor. These models' Inputs: Theoretical Time, Theoretical Velocity and Output: The Current; then with Inputs: Theoretical Time, Theoretical Velocity and Output: The Current, The Voltage; and finally with Inputs: Time, Real Linear Velocity, Rotational Velocity and Output: The Current, The Voltage. Second, a global ANN model with time and speed as inputs and current, voltage, linear speed, rotational speed on carpet, along with current, voltage, linear speed, rotational speed on hard floor. This general model is presented in the form of Simulink model after care selection of number in neurons in hidden layer. This model has the capability to predict and simulate the robot energy characteristics under different conditions. This real data measurements on both hard floor and carpet are presented to be used as training data for Neural Network. All the ANN models are checked in the form of minimum error, accuracy, good regression constants and comparisons between real and predicted data. ANN with feed forward backpropagation technique is used to implement the models. It is adopted to make benefits from its ability of interpolation. ANN models with Back -Propagation (BP) technique is created with suitable numbers of layers and neurons. The last model will be used with the aid of Genetic Algorithm to improve and optimize the energy efficiency of robots.