基于计算机视觉的四足机器人葡萄园自主导航实时航点生成
Computer-Vision Based Real Time Waypoint Generation for Autonomous Vineyard Navigation with Quadruped Robots
AI总结：
本研究针对葡萄园劳动力短缺问题，提出基于计算机视觉的四足机器人导航架构，通过状态机控制实现精准航点生成与葡萄藤修剪作业，实验验证了高可靠性，为果园自动化提供新方案。
摘要：
VINUM项目旨在通过引入前沿移动机器人解决方案，解决现代葡萄园熟练劳动力短缺的问题。该系统借助四足机器人HyQReal的性能，配备机械臂与视觉传感器，可实现自主导航与葡萄藤冬季修剪，减少对人工干预的需求。该方法的核心是一套架构，能让机器人轻松在葡萄园导航，以极高精度识别葡萄藤，并精准靠近以进行修剪。状态机驱动整个流程，在不同阶段间灵活切换，确保任务无缝高效完成。研究通过实验评估系统性能，重点关注航点精度，并针对单株作业优化机器人工作空间。结果表明该架构可靠性很高，HyQReal的平均误差为21.5厘米，标准差为17.6厘米，但要实现最优性能，还需提升葡萄藤检测精度。本研究基于四足机器人在葡萄园中的计算机视觉导航方法，为选择性任务自动化开辟了新可能。该系统架构在理想天气条件下运行良好，可生成并抵达精确航点，最大化所搭载机械臂的工作空间。本工作是我们在意大利机器人与智能机器会议（I-RIM）上发表的短篇论文的扩展。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
