Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality
作者: Daniel Udekwe, Hasan Seyyedhasani
分类: cs.RO, eess.SY
发布日期: 2026-08-27
💡 一句话要点
利用虚拟现实技术提升温室园艺中的人机交互效率
🎯 匹配领域: 支柱一：机器人控制 (Robot Control)
关键词: 虚拟现实 人机交互 机器人技术 温室园艺 土壤湿度评估 植物健康监测 智能农业
📋 核心要点
- 现有的温室园艺监测方法在远程操作和实时反馈方面存在局限，难以有效评估植物健康和土壤状态。
- 本研究提出结合虚拟现实技术与机器人系统进行远程人机交互，以提高叶片检查和土壤湿度评估的效率和准确性。
- 实验结果表明，叶片病害检测准确率达到88%，土壤湿度评估成功率为64.3%，并且植物冠层形态显著影响评估结果。
📝 摘要（中文）
本研究评估了使用虚拟现实技术进行远程人机交互在温室环境中进行叶片检查和土壤湿度评估的有效性。研究中使用的机器人系统包括无人地面车辆和配备摄像头的机械手臂，基于运动学模型进行导航和控制。实验结果显示，叶片检查的循环完成时间在3.3到8.0秒之间，植物病害检测的准确率最高可达88%。土壤湿度评估成功率为64.3%，但在植物冠层形态的影响下，成功率存在显著差异。这些发现表明，摄像头遮挡是主要限制因素，需调整摄像头视角以提升系统的准确性和鲁棒性。
🔬 方法详解
问题定义：本研究旨在解决温室环境中植物健康监测的效率和准确性问题，现有方法在远程操作和实时反馈方面存在不足，导致监测效果不理想。
核心思路：通过结合虚拟现实技术与机器人系统，实现远程人机交互，提升叶片检查和土壤湿度评估的效率，尤其是在复杂环境下的应用。
技术框架：研究中使用的机器人系统包括无人地面车辆和机械手臂，配备摄像头进行数据采集，基于运动学模型进行导航和控制。实验分为叶片检查和土壤湿度评估两个阶段，采用虚拟现实进行远程操作。
关键创新：本研究的创新点在于将虚拟现实技术与机器人系统结合，克服了传统监测方法的局限，尤其是在复杂冠层结构下的有效性。
关键设计：在实验中，针对不同植物的冠层形态设计了不同的摄像头视角和传感策略，以提高数据采集的准确性。实验结果显示，宽叶单冠植物的成功率达到100%，而密集复合冠植物的成功率仅为16.7%。
🖼️ 关键图片
📊 实验亮点
实验结果显示，叶片检查的循环完成时间在3.3到8.0秒之间，病害检测准确率达到88%。土壤湿度评估成功率为64.3%，且植物冠层形态显著影响评估结果，宽叶单冠植物的成功率达到100%。
🎯 应用场景
该研究的潜在应用领域包括温室农业、智能农业监测和机器人远程操作等。通过提升人机交互的效率和准确性，能够为农业生产提供更为精准的决策支持，推动农业智能化发展。未来，该技术有望在更广泛的农业场景中得到应用，提升整体生产效率。
📄 摘要（原文）
This study evaluates the effectiveness of remote human-robot interaction using virtual reality for leaf inspection and soil moisture assessment in a greenhouse environment. The robotic system comprised an unmanned ground vehicle and a robotic manipulator equipped with cameras, governed by kinematic models for navigation and manipulator control. Fourteen distinct plants were inspected across two experiments utilizing VR teleoperation, guided by a set of pre-specified research questions and hypotheses. In the leaf inspection experiments, cycle completion times varied from 3.3 to 8.0 s, and plant-based disease detection was achieved up to 88% accuracy; diseased-spot detection improved numerically in the second experiment, though this change was not statistically significant (p=0.378). For soil moisture assessment, the experiments achieved successful determination of watering needs in up to 64.3% of plants (9 of 14), with consistent success observed for plants 1, 2, 3, 8, 9, 10, and 13; however, this improvement was likewise not statistically significant (p=0.50). A post hoc analysis instead revealed that soil moisture assessment reliability was strongly and significantly predicted by plant canopy morphology (p<0.01): plants with broad, single-leaf canopies reached 100% success by the second experiment, versus only 16.7% for dense, compound canopies. A secondary analysis showed operators became measurably faster at attempting dense-canopy plants without a corresponding gain in success, indicating that camera occlusion, not operator skill or effort, is the dominant limiting factor. These findings show occlusion imposes a sensing limitation rather than a control or training deficiency, and that adapting camera viewpoint and sensing strategy to canopy density is needed to improve the system's accuracy and robustness.