Estimation of Lawn Grass Lengths based on Random Forest Algorithm for Robotic Lawn Mower

Abstract

This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm. This relates to Digital Twin and Virtual Twin of Hybrid Twin approach. Recently, the robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems. However, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. In order to precisely control the rotation speed of motor, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. By applying the random forest algorithm, the combination of sensing parameters attained more than 90% correct estimation ratio is shown through some experiments. Now, the suggested algorithm and the sensor fusion are evaluated against wide range of lawn and grounds.