シミュレーションから実機展開までを統合する情報経路計画アーキテクチャ: GuadalPlanner
A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner
計画・センシング・車両実行のインターフェースを標準化し、同一のアルゴリズムをシミュレーションから実機まで変更なしに評価できる統合アーキテクチャGuadalPlannerを提案し、自律水上艇による水質モニタリングで実証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Alejandro Mendoza Barrionuevo, Dame Seck Diop, Alejandro Casado Pérez, Daniel Gutiérrez Reina, Sergio L. Toral Marín, Samuel Yanes Luis
分類: cs.RO, cs.LG, cs.SE
原文アブストラクト
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.
