  Autonomous navigation is the key to achieving the full automation of
agricultural research and production management (e.g., disease management and
yield prediction) using agricultural robots. In this paper, we introduced a
vision-based autonomous navigation framework for agriculture robots in
trellised cropping systems such as vineyards. To achieve this, we proposed a
novel learning-based method to estimate the path traversibility heatmap
directly from an RGB-D image and subsequently convert the heatmap to a
preferred traversal path. An automatic annotation pipeline was developed to
form a training dataset by projecting RTK GPS paths collected during the first
setup in a vineyard in corresponding RGB-D images as ground-truth path
annotations, allowing a fast model training and fine-tuning without costly
human annotation. The trained path detection model was used to develop a full
navigation framework consisting of row tracking and row switching modules,
enabling a robot to traverse within a crop row and transit between crop rows to
cover an entire vineyard autonomously. Extensive field trials were conducted in
three different vineyards to demonstrate that the developed path detection
model and navigation framework provided a cost-effective, accurate, and robust
autonomous navigation solution in the vineyard and could be generalized to
unseen vineyards with stable performance.
