2022 Volume 25 Issue 3 Pages 240-249
The objective of this research is to estimate the length of the grass and the condition of the ground when a robotic lawn mower runs using the Random Forest and Neural Network, and to change the running control according to the ground conditions for efficient lawn mowing. The main robotic lawn mowers currently on the market cannot recognize the length of the grass or the condition of the ground other than the lawn, such as bare soil. Therefore, the robotic lawn mower always rotates its blade at the maximum speed while running randomly over the specified area. In order to mow the lawn efficiently, we propose to estimate the length of the grass and the ground condition using sensors and machine learning, and to control the mower appropriately. In this study, we experimentally show that the system achieves a correct answer rate of more than 90% in estimating the ground conditions.
