A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner
This addresses the problem of limited transferability between simulation and deployment for researchers and practitioners in robotics and autonomous systems, though it is incremental as it builds on existing technologies.
The paper tackles the challenge of fragmented evaluation pipelines for informative path planning algorithms by introducing a unified architecture that decouples decision-making from vehicle control, enabling consistent testing across simulation and real-world deployment without modifications, as validated through experiments including real-world use on an autonomous surface vehicle for water quality monitoring.
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.