自主机器人实现大范围高精度土壤湿度测绘，省时省力。
MoistureMapper: An Autonomous Mobile Robot for High-Resolution Soil Moisture Mapping at Scale
- 用时域反射仪和钻探装置自动测量土壤含水量。
- 自适应采样使地图误差降低5%，路程减少30%。
- 适合农业精准灌溉和气候研究的大规模应用。
土壤湿度在农业和气候建模中至关重要。现有方法因部署成本过高，难以用于高分辨率传感场景（如变量灌溉）。本文设计并部署了自主移动机器人MoistureMapper，配备时域反射仪（TDR）传感器与直推式钻探机构，可深入土壤测量体积含水量。同时，基于高斯过程建模，实现多种自适应采样策略，构建土壤湿度空间分布图。通过大规模计算仿真与实地验证，自适应策略优于贪婪基准，使行进距离减少30%，重建地图方差降低5%。视频演示见：https://youtu.be/S4bJ4tRzObg
原文摘要 · Abstract (English)
Soil moisture is a quantity of interest in many application areas including agriculture and climate modeling. Existing methods are not suitable for scale applications due to large deployment costs in high-resolution sensing applications such as for variable irrigation. In this work, we design, build and field deploy an autonomous mobile robot, MoistureMapper, for soil moisture sensing. The robot is equipped with Time Domain Reflectometry (TDR) sensors and a direct push drill mechanism for deploying the sensor to measure volumetric water content in the soil. Additionally, we implement and evaluate multiple adaptive sampling strategies based on a Gaussian Process based modeling to build a spatial mapping of moisture distribution in the soil. We present results from large scale computational simulations and proof-of-concept deployment on the field. The adaptive sampling approach outperforms a greedy benchmark approach and results in up to 30\% reduction in travel distance and 5\% reduction in variance in the reconstructed moisture maps. Link to video showing field experiments: https://youtu.be/S4bJ4tRzObg
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