  Autonomous navigation is the foundation of agricultural robots. This paper
focuses on developing an advanced autonomous navigation system for a rover
operating within row-based crops. A position-agnostic system is proposed to
address the challenging situation when standard localization methods, like GPS,
fail due to unfavorable weather or obstructed signals. This breakthrough is
especially vital in densely vegetated regions, including areas covered by thick
tree canopies or pergola vineyards. This work proposed a novel system that
leverages a single RGB-D camera and a Non-linear Model Predictive Control
strategy to navigate through entire rows, adapting to various crop spacing. The
presented solution demonstrates versatility in handling diverse crop densities,
environmental factors, and multiple navigation tasks to support agricultural
activities at an extremely cost-effective implementation. Experimental
validation in simulated and real vineyards underscores the system's robustness
and competitiveness in both standard row traversal and target objects approach.
