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.

翻译：自主车辆信息路径规划算法的评估常因执行流程的碎片化以及仿真与真实部署间的有限可迁移性而受阻。本文提出一种统一架构，将高层决策与车辆特定控制解耦，使得算法能够在不同抽象层级上无需修改即可进行一致性评估。该架构通过GuadalPlanner实现，其定义了规划、感知与车辆执行间的标准化接口。这是一个开放且可扩展的研究工具，支持基于离散图的环境与可互换的规划策略，并构建于广泛采用的机器人技术之上，包括ROS2、MAVLink与MQTT。其设计允许相同的算法逻辑通过完全一致的执行流程，部署于全仿真环境、软件在环配置以及物理自主车辆中。该方法的有效性通过一系列实验得到验证，包括在搭载实时传感器反馈、执行水质监测任务的自主水面艇上进行的真实场景部署。