让小型农用机器人在作物下自主导航，解决信号丢失难题。
CropNav: a Framework for Autonomous Navigation in Real Farms
- 根据环境自动切换激光雷达或航点路径追踪模式
- 每干预一次可多走750米，比纯GNSS方案提升显著
- 能自动检测失败并恢复，减少人工介入
能在植物冠层下运行的小型机器人可为农业带来新可能。然而，与大型自动驾驶拖拉机不同，这类冠层内机器人的自主导航仍面临挑战，因全球导航卫星系统（GNSS）在冠层下信号不可靠。本文提出一种混合导航系统，能自主切换不同感知模态，实现田间内外的完整路径导航。通过选择合适的路径参考源，机器人可应对GNSS信号质量下降，并利用行作物结构实现自主导航。该系统还能自动检测导航失败并实现恢复，延长自主运行时间，减少人工干预。实验显示，相比基于GNSS的导航，本系统每次干预可多行驶约750米；相比仅依赖行追踪的导航，可多行驶约500米。
原文摘要 · Abstract (English)
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.
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