Headland Turn Automation Concept for Tractor-Trailer System with Deep Reinforcement Learning

Navigation of agricultural vehicles along predefined paths using control and path planning strategies has been extensively studied, but some cases require challenging maneuvers that cannot be manually predefined. An example of such cases in agriculture is the headland turning process, which heavily depends on the field geometry, tractor, and implement. Therefore, automating this process can improve time efficiency, optimize land use, and reduce the cognitive burden on semi-autonomous tractor drivers. Reinforcement Learning (RL) is a powerful framework for learning complex policies in high-dimensional environments, making it widely used in autonomous driving. In this paper, we investigate the effectiveness of recent model-free RL algorithms, specifically policy optimization and Q-learning types, for headland turning of a tractor-trailer combination. The findings show that reinforcement learning-based approaches are effective for
learning a headland turn maneuver and provide insights into which state-of-the-art method has a superior performance in learning a headland turn maneuver.