面向户外移动机器人的可现场部署的基于GNSS的导航栈

中文摘要 AI 辅助

户外机器人不仅需要高精度的接收器和路径跟踪律：导航系统必须保持从地理航路点到执行器命令的几何一致性，暴露测量有效性和时间信息，并响应无效或过时的状态信息。本工作提出了一个ROS 2导航栈，具有可互换的单GNSS-IMU和双天线GNSS定位前端。两者都为纯追踪、虚拟点横向PID、有限时域非线性模型预测控制（NMPC）和分段相关混合调度器提供了共同的局部东-北-天状态接口。该架构规定了坐标约定、基准初始化、异步状态构建、航路点几何、控制器方程、质量门、命令仲裁和看门狗行为。2025年和2026年葡萄园部署期间收集的独立物理现场运行支持对800次运行的平衡评估，在约199.6米的路线上，八种控制器-定位组合各进行100次运行。行混合模式在评估数据集中产生了最低的运行平均采集后平均绝对横向误差（MAE）：单GNSS+IMU为0.00952米，双GNSS为0.00846米。这些发现描述了在评估条件下记录位置与参考路线的偏差。开源导航软件和部署说明可在本文的URL中获取。

英文摘要

Outdoor robots require more than an accurate receiver and a path-tracking law: the navigation system must preserve geometric consistency from geographic waypoints to actuator commands, expose measurement validity and timing, and respond to invalid or stale state information. This work presents a ROS~2 navigation stack with interchangeable single-GNSS--IMU and dual-antenna-GNSS localization front ends. Both provide a common local East--North--Up state interface for pure pursuit, virtual-point cross-track PID, finite-horizon nonlinear model predictive control (NMPC), and a segment-dependent hybrid dispatcher. The architecture specifies coordinate conventions, datum initialization, asynchronous state construction, waypoint geometry, controller equations, quality gates, command arbitration, and watchdog behavior. Independent physical field runs collected during 2025 and 2026 grape-vineyard deployments support a balanced evaluation of 800 runs, with 100 runs for each of eight controller--localization combinations on an approximately 199.6-m route. The row-hybrid mode yields the lowest run-averaged post-acquisition mean absolute cross-track error (MAE) in the evaluated dataset: 0.00952~m with single GNSS+IMU and 0.00846~m with dual GNSS. These findings characterize deviations of the recorded positions from the reference route under the evaluated conditions. The open-source navigation software and deployment instructions are available in the https://github.com/YiyuanLinXX/PPBv2/tree/main/PPBv2_Navigation.

发表机构

• Cornell University（康奈尔大学）
• Cornell AgriTech（康奈尔农业科技学院）

机构由 AI 辅助整理，请以论文原文为准。