用视觉导航的机器人在仿真中实现高精度棉花采摘，有望降低农业成本与环境影响。
CottonSim: A vision-guided autonomous robotic system for cotton harvesting in Gazebo simulation
- 基于YOLOv8nseg和深度相机实现棉花实例分割，识别准确率超93%
- GPS与地图双导航方案分别达100%和96.7%任务完成率，误差小于0.25米
- 开源仿真系统支持农业机器人研发，适合农业自动化研究者使用
棉花是美国主要经济作物，全美棉花生产集中于南部17个州。当前采摘环节面临成本高、依赖有害脱叶剂及大型机械等问题，导致产量损失、纤维质量下降和土壤板结，威胁可持续性。为此，本研究提出一种轻量化小型视觉引导自主机器人采摘系统。系统基于Clearpath Husky平台集成CottonEye感知模块，在Gazebo仿真环境中测试。构建虚拟棉田用于自主导航验证。导航采用GPS与地图双模方式，结合RGB-D相机与YOLOv8nseg实例分割模型，mAP达85.2%，召回率88.9%，精确率93.0%。GPS方案在(5e-6)°阈值下实现100%任务完成率，地图方法在0.25米阈值下达成96.7%任务完成率。基于ROS开发的仿真包可稳定支持自主采摘模拟，为农业机器人提供可扩展基准。代码与数据集已开源：https://github.com/imtheva/CottonSim
原文摘要 · Abstract (English)
Cotton is a major cash crop in the United States, with the country being a leading global producer and exporter. Nearly all U.S. cotton is grown in the Cotton Belt, spanning 17 states in the southern region. Harvesting remains a critical yet challenging stage, impacted by the use of costly, environmentally harmful defoliants and heavy, expensive cotton pickers. These factors contribute to yield loss, reduced fiber quality, and soil compaction, which collectively threaten long-term sustainability. To address these issues, this study proposes a lightweight, small-scale, vision-guided autonomous robotic cotton picker as an alternative. An autonomous system, built on Clearpath's Husky platform and integrated with the CottonEye perception system, was developed and tested in the Gazebo simulation environment. A virtual cotton field was designed to facilitate autonomous navigation testing. The navigation system used Global Positioning System (GPS) and map-based guidance, assisted by an RGBdepth camera and a YOLOv8nseg instance segmentation model. The model achieved a mean Average Precision (mAP) of 85.2%, a recall of 88.9%, and a precision of 93.0%. The GPS-based approach reached a 100% completion rate (CR) within a $(5e-6)^{\circ}$ threshold, while the map-based method achieved a 96.7% CR within a 0.25 m threshold. The developed Robot Operating System (ROS) packages enable robust simulation of autonomous cotton picking, offering a scalable baseline for future agricultural robotics. CottonSim code and datasets are publicly available on GitHub: https://github.com/imtheva/CottonSim
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