[论文解读] A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner

Alejandro Mendoza Barrionuevo, Dame Seck Diop+4 more|arXiv (Cornell University)|Feb 11, 2026

Robotic Path Planning Algorithms被引用 0

一句话总结

本文提出 GuadalPlanner，这是一个统一的端到端IPP架构，将高层决策与车辆控制解耦，使从仿真到现实部署的测试具有可重复性。

ABSTRACT

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.

研究动机与目标

• 将高层IPP决策与车辆特定执行解耦，以实现跨层次评估。
• 提供用于规划、感知与执行的标准化、模块化接口，覆盖仿真、SITL(软件在环)与现实平台。
• 提供一个开放、可扩展的工具，支持基于图的多智能体IPP。
• 弥合算法开发、基于仿真的测试与现场部署之间的差距。
• 促进跨研究团队的IPP算法的可重复性和可迁移性。

提出的方法

• 引入三层执行架构：算法层仿真、SITL（软件在环）以及真实部署。
• 定义标准化的车队接口、远程车队接口和基于图的环境接口，以实现规划与执行的解耦。
• 将 GuadalPlanner 实现为基于ROS2的中间件，集成 MAVLink/MAVROS，并通过MQTT实现跨平台执行。
• 利用 ArduPilot SITL实现真实的动力学，与各抽象层保持一致的规划逻辑。
• 提供开源实现及示例和部署配置，便于复用和可比性比较。

Figure 1: Diagram of the GuadalPlanner implementation. It is structured around a hierarchical architecture of classes that progresses from general to specific.

实验结果

研究问题

• RQ1 如何将高层IPP决策从车辆特定控制中解耦，以实现跨仿真和真实部署的一致评估？
• RQ2 一个统一接口架构是否能够支持从算法开发到现场部署的端到端IPP实验，而无需代码重写？
• RQ3 对跨多个平台和智能体的可重复IPP研究，标准化、开放工具有哪些优势与能力？
• RQ4 GuadalPlanner在不同IPP任务的算法层、SITL和真实ASV部署中的表现如何？

主要发现

• GuadalPlanner 提供一个统一的端到端IPP实验平台，在不进行架构重写的情况下实现从仿真到真实部署的桥接。
• 该架构支持离散的基于图的环境、可互换的规划策略、多智能体配置以及实时传感器反馈。
• 在三个抽象层级的实验验证中，包括在自主水面车辆上的真实部署，证明了方法的有效性。
• 开源实现结合ROS2、MAVLink和MQTT，使跨团队可重复、可迁移的IPP实验成为可能。
• 系统能够在标准化接口内处理多种IPP任务，如水质监测、污染物分布绘制和垃圾收集等。

Figure 2: Internal structure of the proposed middleware architecture, illustrating the interaction between planning, communication, and vehicle interfaces implemented over ROS2. The green arrows corresponds to external interfaces to other peripherals.