统一仿真与实车路径规划架构，实现算法无缝迁移
A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner
- 解耦决策与控制，用标准化接口打通仿真到实车流程
- 同套代码可在仿真、软件在环、实车三场景运行
- 已在无人水面艇水质监测中验证，支持实时传感反馈
自动驾驶车辆的信息感知路径规划评估常受限于执行流程碎片化及仿真与实车部署间的迁移困难。本文提出一种统一架构，将高层决策与车辆控制解耦，使算法可在不同抽象层级间一致评估而无需修改。该架构通过GuadalPlanner实现，定义了规划、感知与车辆执行间的标准化接口。它是一个开放可扩展的研究工具，支持离散图结构环境和可替换的规划策略，基于ROS2、MAVLink和MQTT等通用机器人技术构建。同一算法逻辑可直接部署于全仿真环境、软件在环配置及物理自动驾驶车辆，使用相同执行流程。通过一系列实验验证，包括在无人水面艇上进行水质监测的实车部署，实现传感器实时反馈。
原文摘要 · Abstract (English)
The evaluation of informative path planning algorithms for autonomous vehicles is often hindered by fragmented execution pipelines and limited transferability between simulation and real-world deployment. This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control, enabling algorithms to be evaluated consistently across different abstraction levels without modification. The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution. It is an open and extensible research tool that supports discrete graph-based environments and interchangeable planning strategies, and is built upon widely adopted robotics technologies, including ROS2, MAVLink, and MQTT. Its design allows the same algorithmic logic to be deployed in fully simulated environments, software-in-the-loop configurations, and physical autonomous vehicles using an identical execution pipeline. The approach is validated through a set of experiments, including real-world deployment on an autonomous surface vehicle performing water quality monitoring with real-time sensor feedback.
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