发表机构
Paris Research, Sony Computer Science Laboratories（索尼计算机科学实验室巴黎研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种结合3D重建、几何分析与运动规划的自主机器人平台，用于对植物叶片进行靶向荧光测量，实现自动化的空间分辨生理测量，为高分辨率植物表型及自主农业检测提供新方法。
AI 中文摘要
尽管大多数表型平台主要依赖基于图像的测量，但先进的植物表征需要整合主动生理传感模态，如叶绿素荧光。我们提出一种自主机器人平台，旨在对植物叶片进行靶向荧光测量。该系统结合3D植物重建、几何分析和运动规划，以定位合适的测量点并为机械臂生成无碰撞轨迹。从多视图数据重建植物的密集3D模型，并基于朝向、可达性和传感约束提取候选叶片表面。这些目标随后被整合到任务级规划框架中，该框架引导末端执行器达到基于点的荧光采集所需的精确接触或近接触配置。该平台可实现超越被动成像的自动化、可重复且空间分辨的生理测量。通过将感知、几何推理与操纵紧密结合，所提系统为高分辨率植物表型提供了一种机器人驱动的方法，并为自主农业检测和植物感知操纵开辟了新方向。
英文摘要
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.
CommentsUR2026
