  The development of precision agriculture has gradually introduced automation
in the agricultural process to support and rationalize all the activities
related to field management. In particular, service robotics plays a
predominant role in this evolution by deploying autonomous agents able to
navigate in fields while executing different tasks without the need for human
intervention, such as monitoring, spraying and harvesting. In this context,
global path planning is the first necessary step for every robotic mission and
ensures that the navigation is performed efficiently and with complete field
coverage. In this paper, we propose a learning-based approach to tackle
waypoint generation for planning a navigation path for row-based crops,
starting from a top-view map of the region-of-interest. We present a novel
methodology for waypoint clustering based on a contrastive loss, able to
project the points to a separable latent space. The proposed deep neural
network can simultaneously predict the waypoint position and cluster assignment
with two specialized heads in a single forward pass. The extensive
experimentation on simulated and real-world images demonstrates that the
proposed approach effectively solves the waypoint generation problem for both
straight and curved row-based crops, overcoming the limitations of previous
state-of-the-art methodologies.
