论文标题
机器人灌溉水管理：通过感觉和外观估算土壤水分含量
Robotic Irrigation Water Management: Estimating Soil Moisture Content by Feel and Appearance
论文作者
论文摘要
在本文中，我们建议在结构化机器人温室环境中进行灌溉水管理（IWM）的机器人系统。商业上可用的机器人操纵器配备了RGB-D相机和土壤水分传感器。两者用于自动化称为“感觉和外观方法”的过程，这是一种监测土壤水分以确定何时灌溉以及要涂多少水的方法。我们开发了一个合规的力控制框架，使机器人能够将土壤水分传感器插入土壤敏感的植物根部区域，而不会损害植物。 RGB-D相机用于大致估算土壤表面，以计划土壤采样方法。该相机与开发的自适应力控制算法一起使用，使机器人可以先验地品尝土壤，而不必先验地进行土壤刚度。最后，我们假设一种基于深度学习的方法利用相机在视觉上评估土壤健康和水分含量。
In this paper we propose a robotic system for Irrigation Water Management (IWM) in a structured robotic greenhouse environment. A commercially available robotic manipulator is equipped with an RGB-D camera and a soil moisture sensor. The two are used to automate the procedure known as "feel and appearance method", which is a way of monitoring soil moisture to determine when to irrigate and how much water to apply. We develop a compliant force control framework that enables the robot to insert the soil moisture sensor in the sensitive plant root zone of the soil, without harming the plant. RGB-D camera is used to roughly estimate the soil surface, in order to plan the soil sampling approach. Used together with the developed adaptive force control algorithm, the camera enables the robot to sample the soil without knowing the exact soil stiffness a priori. Finally, we postulate a deep learning based approach to utilize the camera to visually assess the soil health and moisture content.
