A Field-Deployable GNSS-based Navigation Stack for Outdoor Mobile Robots

2026-09-23

AI Summary

This study addresses the challenges of geometric inconsistency, frequent invalid states, and insufficient navigation accuracy encountered by outdoor robots operating in complex environments. To this end, we propose a modular autonomous navigation framework based on ROS 2. The methodology features interchangeable localization front-ends and a hybrid control architecture that integrates single- and dual-antenna GNSS-IMU localization with pure pursuit, virtual-point PID, and nonlinear model predictive control (NMPC). System robustness is ensured through strict coordinate conventions, quality gating, and watchdog mechanisms. Field evaluations conducted in a vineyard demonstrate that the row-hybrid mode achieves an average lateral error of merely 8.5 to 9.5 millimeters, thereby validating the high precision and reliability of the proposed architecture.

Abstract

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.

Problem

• outdoor mobile robots
• GNSS navigation
• geometric consistency
• state validity
• ROS 2

Innovation

• ROS 2 navigation stack
• interchangeable GNSS localization
• nonlinear model predictive control
• hybrid dispatcher
• field-deployable robotics