Detection of motor skidding in autonomous lawn mowers : Detection of skidding in autonomous lawn mower using machine learning technique MLP with wheel motor currents and IMU.

Abstract: Purpose – The purpose of the thesis is to evaluate the accuracy of two different approaches of how to detect skidding in autonomous lawn mowers. Using the motor currents and inertia sensors from the mower, a neural network is applied to predict if there was a skidding or not. The usefulness of knowing when a skid happens could be of value for future developments making better autonomous decision making. Method – The thesis will adopt the methodology process of Design Science Research (DSR). The study begins with bringing awareness of the problem by previous knowledge and related works to skidding in wheeled robots. Thereafter an experiment is set up to generate data when the autonomous lawn mower is in conditions of skidding and non-skidding. The data collected will be processed with machine learning algorithm, multilayer perceptron. Findings – The findings showed high accuracies in both techniques where adding an IMU sensor in addition to motor currents showed higher accuracy then only using motor currents. Both techniques showed low number of false detections and near zero missed detections which is a preferred feature, the behavior of the autonomous lawn mower benefits more from a false detection than not detecting any at all and get stuck. Implications – The autonomous lawn mowers of today have a tendency of failing in the yard due to skidding in uneven or slippery terrain. The robot either reacts by assuming a collision has happened or gets no traction and gets stuck. A first step to solve this problem is by detecting such a skid to then be able to take action. Limitations – The results will be limited to the autonomous lawn mower of Globe Group as the data collection is made with an autonomous lawn mower of Globe Group. The mower will run on a flat outdoor grass lawn to maintain the experiment on a reasonable level for a bachelor thesis. Keywords – robot, autonomous, lawn mower, machine learning, detection, skidding