A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner

TL;DR

This paper presents GuadalPlanner, a unified architecture that enables consistent evaluation and deployment of informative path planning algorithms across simulation and real-world autonomous vehicle platforms.

Contribution

It introduces GuadalPlanner, a standardized, extensible framework that decouples decision-making from vehicle control, facilitating transferability from simulation to physical deployment.

Findings

Successful real-world deployment on an autonomous surface vehicle.

Supports multiple environments and planning strategies.

Enables consistent evaluation across simulation and physical systems.

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.