  Precision agriculture is rapidly attracting research to efficiently introduce
automation and robotics solutions to support agricultural activities. Robotic
navigation in vineyards and orchards offers competitive advantages in
autonomously monitoring and easily accessing crops for harvesting, spraying and
performing time-consuming necessary tasks. Nowadays, autonomous navigation
algorithms exploit expensive sensors which also require heavy computational
cost for data processing. Nonetheless, vineyard rows represent a challenging
outdoor scenario where GPS and Visual Odometry techniques often struggle to
provide reliable positioning information. In this work, we combine Edge AI with
Deep Reinforcement Learning to propose a cutting-edge lightweight solution to
tackle the problem of autonomous vineyard navigation without exploiting precise
localization data and overcoming task-tailored algorithms with a flexible
learning-based approach. We train an end-to-end sensorimotor agent which
directly maps noisy depth images and position-agnostic robot state information
to velocity commands and guides the robot to the end of a row, continuously
adjusting its heading for a collision-free central trajectory. Our extensive
experimentation in realistic simulated vineyards demonstrates the effectiveness
of our solution and the generalization capabilities of our agent.

Robotics;Artificial Intelligence