Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

Abstract

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.

Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System

“…DL has been used in many robotics applications where objects must be detected [ 2 ] or segmented [ 3 ] to address manipulation tasks, demonstrating competitive advantages compared to classic image processing algorithms in terms of accuracy and robustness. For instance, relevant works in the precision agriculture field have been proposed in recent years to support autonomous navigation [ 4 , 5 ], harvesting [ 6 ], and spraying [ 7 ]. Intelligent DL-based behaviors are also desired for visual-based robotic surgery to detect and segment instruments [ 8 , 9 ], and in many industrial robotic tasks [ 10 , 11 ].…”