The VINUM project seeks to address the shortage of skilled labor in modern vineyards by introducing a cutting-edge mobile robotic solution. Leveraging the capabilities of the quadruped robot, HyQReal, this system, equipped with arm and vision sensors, offers autonomous navigation and winter pruning of grapevines reducing the need for human intervention. At the heart of this approach lies an architecture that empowers the robot to easily navigate vineyards, identify grapevines with unparalleled accuracy, and approach them for pruning with precision. A state machine drives the process, deftly switching between various stages to ensure seamless and efficient task completion. The system's performance was assessed through experimentation, focusing on waypoint precision and optimizing the robot's workspace for single-plant operations. Results indicate that the architecture is highly reliable, with a mean error of 21.5cm and a standard deviation of 17.6cm for HyQReal. However, improvements in grapevine detection accuracy are necessary for optimal performance. This work is based on a computer-vision-based navigation method for quadruped robots in vineyards, opening up new possibilities for selective task automation. The system's architecture works well in ideal weather conditions, generating and arriving at precise waypoints that maximize the attached robotic arm's workspace. This work is an extension of our short paper presented at the Italian Conference on Robotics and Intelligent Machines (I-RIM).

翻译：VINUM项目旨在通过引入先进的移动机器人解决方案，解决现代葡萄园中熟练劳动力短缺的问题。该系统借助四足机器人HyQReal的能力，配备机械臂和视觉传感器，可实现葡萄园的自主导航与冬季修剪，从而减少人工干预需求。该方案的核心架构赋予机器人轻松导航葡萄园、以无与伦比的精度识别葡萄植株，并精准接近植株进行修剪的能力。状态机驱动整个流程，在不同阶段间灵活切换，确保任务完成的无缝性与高效性。通过实验评估系统性能，重点关注路径点精度及优化机器人在单株作业中的工作空间。结果表明，该架构具有高度可靠性：HyQReal的平均误差为21.5厘米，标准差为17.6厘米。但为达到最优性能，仍需改进葡萄植株检测精度。本工作基于四足机器人在葡萄园中基于计算机视觉的导航方法，为选择性任务自动化开辟了新可能。该系统的架构在理想天气条件下表现良好，能生成并抵达精准的路径点，最大化搭载机械臂的工作空间。本文是对我们此前在意大利机器人与智能机器会议（I-RIM）发表的短篇论文的扩展。