  Agricultural robots have the potential to increase production yields and
reduce costs by performing repetitive and time-consuming tasks. However, for
robots to be effective, they must be able to navigate autonomously in fields or
orchards without human intervention. In this paper, we introduce a navigation
system that utilizes LiDAR and wheel encoder sensors for in-row, turn, and
end-row navigation in row structured agricultural environments, such as
vineyards. Our approach exploits the simple and precise geometrical structure
of plants organized in parallel rows. We tested our system in both simulated
and real environments, and the results demonstrate the effectiveness of our
approach in achieving accurate and robust navigation. Our navigation system
achieves mean displacement errors from the center line of 0.049 m and 0.372 m
for in-row navigation in the simulated and real environments, respectively. In
addition, we developed an end-row points detection that allows end-row
navigation in vineyards, a task often ignored by most works.
