Precision maps are useful in agricultural farm management for providing farmers (and field researchers) with locational information. Having an environmental model that includes geo‐referenced data would facilitate the deployment of multi‐robot systems, that has emerged in precision agriculture. It further allows developing automated techniques and tools for map reconstruction and field coverage for farming purposes. In this study, a reinforcement learning‐based method (and in particular dyna‐Q+) is presented for a team of unmanned ground vehicles (UGVs) to cooperatively learn an unknown dynamic field (and in particular, an agricultural field). The problem we address here is to deploy UGVs to map plant rows, find obstacles, whose locations are not known a priori, and define regions of interest in the field (e.g., areas with high water stress). Once an environment model is built, the UGVs are then distributed to provide full coverage of plants and update the reconstructed map simultaneously. Simulation results are finally presented to demonstrate that the proposed method for simultaneous learning and planning can successfully learn a model of the field and monitor the coverage area.

中文翻译：

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一种基于强化学习的方法，用于使用一组自主地面车辆对未知领域进行建模和覆盖

精确地图在农业农场管理中非常有用，可为农民（和实地研究人员）提供位置信息。拥有包含地理参考数据的环境模型将有助于部署在精准农业中出现的多机器人系统。它还允许开发用于农业目的的地图重建和田间覆盖的自动化技术和工具。在这项研究中，提出了一种基于强化学习的方法（尤其是 dyna-Q+），供无人地面车辆 (UGV) 团队合作学习未知的动态领域（尤其是农业领域）。我们在这里解决的问题是部署 UGV 来绘制植物行的地图，找到位置未知的障碍物，并定义现场感兴趣的区域（例如，高水压力区域）。一旦建立了环境模型，就会分配 UGV 以提供植物的全覆盖并同时更新重建的地图。最后给出了仿真结果，以证明所提出的同步学习和规划方法可以成功地学习现场模型并监控覆盖区域。