CropNav: a Framework for Autonomous Navigation in Real Farms

TL;DR

CropNav introduces a hybrid autonomous navigation system for small farm robots that effectively switches between sensing modalities to operate reliably inside and outside crop fields, overcoming GNSS limitations.

Contribution

The paper presents a novel hybrid navigation framework that automatically switches between sensing modalities and detects failures to enhance autonomous farm robot operation.

Findings

750 m improvement over GNSS-based navigation per intervention

500 m improvement over row-following navigation per intervention

Effective automatic failure detection and recovery in navigation

Abstract

Small robots that can operate under the plant canopy can enable new possibilities in agriculture. However, unlike larger autonomous tractors, autonomous navigation for such under canopy robots remains an open challenge because Global Navigation Satellite System (GNSS) is unreliable under the plant canopy. We present a hybrid navigation system that autonomously switches between different sets of sensing modalities to enable full field navigation, both inside and outside of crop. By choosing the appropriate path reference source, the robot can accommodate for loss of GNSS signal quality and leverage row-crop structure to autonomously navigate. However, such switching can be tricky and difficult to execute over scale. Our system provides a solution by automatically switching between an exteroceptive sensing based system, such as Light Detection And Ranging (LiDAR) row-following navigation and waypoints path tracking. In addition, we show how our system can detect when the navigate fails and recover automatically extending the autonomous time and mitigating the necessity of human intervention. Our system shows an improvement of about 750 m per intervention over GNSS-based navigation and 500 m over row following navigation.