Design and Testing of Headland Turning Algorithms Based on Transition Distance Prediction for Autonomous Rice Transplanter

Highlights Autonomous rice transplanters often deviate during the transition between turning and straight paths. Headland turning path planning algorithms were designed to reduce transition distances before and after turning. Transition distances were predicted based on lateral deviations and heading errors over a threshold using simulations. The developed algorithms were validated for reducing transition distance with an auto-guidance system in a paddy field. Abstract. In the straight sections before and after headland turning in paddy fields, high lateral deviations and heading errors may occur due to changes in the path curvature and dynamic factors of the vehicle and path tracking algorithm. In the case of autonomous rice transplanters, such errors may lengthen the transitions between turning and straight sections and deteriorate working efficiency. In this study, headland turning path generation algorithms for rice transplanters were proposed to reduce the transition distances. Path models were defined considering the transition distances to move the vehicle such that the error was within the acceptable range. The transition distances were predicted by using a simulator that employed a kinematic vehicle model and a tracking algorithm with a model-based control system for generating steering angles. A look-ahead search method and tilt compensation for the Global Navigation Satellite System were applied as well. The developed method was validated in a paddy field by using a Daedong DRP60D rice transplanter. The transition distance decreased by 34.04% compared to that associated with the baseline path that did not consider the transitions. The overlap area decreased by an average of 55.51% within the straight lines. During straight operation, the maximum lateral deviation did not exceed 15 cm. These results confirmed that the proposed algorithms for autonomous rice transplanters can traverse a target path more accurately before and after turning and, consequently, increase work efficiency by avoiding non-planted areas. Keywords: Auto-guidance, Headland turning, Path planning, Prediction, Rice transplanter, Transition.