用机器人自动测叶片荧光，精准定位植物应激差异。
Active sensing to characterize the heterogeneity of plant stress
- 通过3D重建与几何分析，自动找合适测量点。
- 实现无碰撞轨迹规划，精确采集荧光数据。
- 适合需要高精度生理表型的农业科研人员。
现有表型平台多依赖图像测量，而精准植物表征需融合主动生理传感，如叶绿素荧光。本文提出一种自主机器人平台，可对植物叶片进行靶向荧光测量。系统结合多视角3D植物重建、几何分析与运动规划，定位适宜测量点并生成无碰撞机械臂轨迹。基于3D模型提取符合朝向、可达性及传感约束的候选叶面，集成至任务级规划框架，引导末端执行器到达精确接触或近接触位置以获取点式荧光数据。该平台实现自动化、可重复、空间分辨的生理测量，突破被动成像局限。通过感知、几何推理与操作的紧密耦合，提供基于机器人的高分辨率植物表型新范式，为自主农业检测与植物感知操控开辟新方向。
原文摘要 · Abstract (English)
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.
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