Tractor Automated Ground Leveling (AGL) Simulation using Artificial Neural Network

Abstract

Traditional tractor ground leveling operation applies a manual process with no electronic assistance. Automated Ground Leveling (AGL) will increase quality of leveling and operator comfort. This paper outlines a machine learning approach using Artificial Neural Network (ANN). The proposed AGL uses tractor inclined angle and leveling error as inputs. The target output is the tractor scraper implement raise or lower command. The equations to run simulations are formulated and applied to the model and verified during simulations. The details can be found in section IV of this paper. John Deere StarFire 6000 GPS receiver is proposed to be the device to obtain latitude, longitude, altitude and an IMU device to obtain pitch/angling data of tractor. The proposed inputs and target output proved to be effective in producing a set of weights and biases that learns to control the scraper implement. Twenty (20) ANN trainings were conducted using the same set of training data. Out of the twenty trainings, three sets of trained weights and biases outperformed the training set. The best trained weights and biases produced an RMS error of 0.50449 compared to human training data RMS error of 0.593, which was about 14.9% improvement. The algorithm recognizes the goal of staying close to the ground reference line. This paper provides a brief review on ANN for clarity and applies it to the AGL.