CropNav: a Framework for Autonomous Navigation in Real Farms

TL;DR

This work presents a hybrid navigation system that autonomously switches between different sets of sensing modalities to enable full field navigation, both inside and outside of crop.

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.

Figures (9)

• Figure 1: We present a solution to autonomously navigate in farm environments. By fusing multiple sensors, our system is able to smartly switch between reference modalities to provide safe navigation in real agricultural scenarios.
• Figure 2: CropNav system diagram. Multiple sensors are used to fuse information and generate three different paths: in-row, out-row and recovery path. A navigation supervisor receives information from the perception system and the state estimation. Based on this information, the supervisor selects the appropriate path to provide safe navigation in the farm environment.
• Figure 3: Waypoints recording. Yellow circles are examples of how the waypoints are recorded. The red lines represent the reference path automatically created from these points.
• Figure 4: Model predictive control. The diagram explains the controller responsible to track a reference state path.
• Figure 5: Experiment with CropNav. In this experiment, our method runs trough six crop rows. Plot shows the navigated path with recoveries and failures locations.