Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a fundamental requirement to employ service robotics in most fields of application. Among all, researchers in the precision agriculture domain strive to devise robust and cost-effective autonomous platforms in order to provide genuinely large-scale competitive solutions. In this article, we present a complete algorithmic pipeline for row-based crops autonomous navigation, specifically designed to cope with low-range sensors and seasonal variations. Firstly, we build on a robust data-driven methodology to generate a viable path for the autonomous machine, covering the full extension of the crop with only the occupancy grid map information of the field. Moreover, our solution leverages on latest advancement of deep learning optimization techniques and synthetic generation of data to provide an affordable solution that efficiently tackles the well-known Global Navigation Satellite System unreliability and degradation due to vegetation growing inside rows. Extensive experimentation and simulations against computer-generated environments and real-world crops demonstrated the robustness and intrinsic generalizability of our methodology that opens the possibility of highly affordable and fully autonomous machines.

翻译：昂贵传感器与低效算法流水线显著增加了自主机器的总体成本。然而，经济实用的机器人解决方案对实际应用至关重要，其经济影响构成了在大多数应用领域部署服务机器人的基本要求。其中，精准农业领域的研究人员致力于开发稳健且成本效益高的自主平台，以提供真正大规模且具有竞争力的解决方案。本文提出了一套完整的农作物行间自主导航算法流水线，专为应对低精度传感器与季节性变化而设计。首先，我们基于稳健的数据驱动方法，仅利用田地的占用网格图信息，为自主机器生成覆盖整片农作物的可行路径。此外，我们的解决方案充分利用深度学习优化技术的最新进展与数据合成生成，提供了一种经济高效的方案，有效解决了因行间植被生长导致的全球导航卫星系统（Global Navigation Satellite System）常见不可靠性与性能下降问题。通过大量针对计算机生成环境与真实农作物的实验与仿真，验证了我们方法的稳健性与内在泛化能力，为实现高度经济且完全自主的机器开辟了可能性。