We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).
翻译:我们提议建立一个新型混合电缆机器人,配有操纵器和相机,用于高精度、中度通量植物监测,作为一个例子,在垂直水栽农场中,我们建议采用新型混合电缆机器人,用于高精度、中度通量植物监测,并示范性地展示非破坏性植物质量估计;高时空分辨率和空间分辨率的植物监测对农民和研究人员都很重要,以探测异常现象和开发植物生长的预测模型;提供高质量、现成结构自动(SfM)和摄影测量包,使得一个充满活力的机器人学家群体能够将计算机愿景应用于非破坏性植物监测。虽然现有的方法往往侧重于高通量(例如卫星、无人驾驶航空飞行器(UAAV)、车辆升降压、传送带光束图像)或高精确度的植物监测(例如转式扫描仪或机器人臂臂),我们建议一个中层,通过中等通量的透力、高度自动化的机器人,我们的设计配对工作空间的变缩方法(CDPR),同时进行高压的计算,同时进行第4度的机械模型的模拟(我们从最接近的模型的模型的自我分析, 展示,我们最接近性地展示了我们最接近的模型的模型的模型的模型的模型的模型的模型的模型的模型, 和最接近性能的模型的模型的模型的模型的模型的模型, 展示。