Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower

Abstract

This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. 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. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.