Trajectory-Based Algorithm for the Quantitative Evaluation of Automatically Steered Vehicles

Abstract

During the last ten years, automatic guidance systems have become more common in agricultural vehicles. However, users of auto-guided systems can be confused by the growing variety of options commercially available, as well as by the guidance accuracies advertised by different manufacturers. This work proposes an algorithm to evaluate the performance of auto-steered machines for any kind of vehicle and any type of guidance system in the general case of straight row and curved row guidance. The core of the algorithm is based on the comparison of two trajectories: reference course and actual path. The algorithm searches in a neighboring area for the reference points and calculates the deviations. Statistical analysis of the errors provides quantitative information to evaluate the behavior of auto-steered vehicles. The advantage of this technique rests on its independence of regression methods and its immunity to outliers. This methodology was applied to an automatically guided self-propelled forage harvester at both low-speed and high-speed guidance. Results are presented, and when compared to those obtained applying conventional linear regression, they show a slighter impact of outliers and a more efficient procedure and data management.