AROLA: スケールド自律レーシングのためのモジュール式階層アーキテクチャ
AROLA: A Modular Layered Architecture for Scaled Autonomous Racing
スケールド自律レーシング向けに、ROS 2で標準化された交換可能なレイヤとコンポーネントからなるモジュール式ソフトウェアアーキテクチャAROLAと、性能評価を標準化するRace Monitorを提案し、シミュレーションと実機で検証した。
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: Fam Shihata, Mohammed Abdelazim, Ahmed Hussein
分類: cs.RO, cs.SE
原文アブストラクト
Autonomous racing has advanced rapidly, particularly on scaled platforms, and software stacks must evolve accordingly. In this work, AROLA is introduced as a modular, layered software architecture in which fragmented and monolithic designs are reorganized into interchangeable layers and components connected through standardized ROS 2 interfaces. The autonomous-driving pipeline is decomposed into sensing, pre-processing, perception, localization and mapping, planning, behavior, control, and actuation, enabling rapid module replacement and objective benchmarking without reliance on custom message definitions. To support consistent performance evaluation, a Race Monitor framework is introduced as a lightweight system through which lap timing, trajectory quality, and computational load are logged in real time and standardized post-race analyses are generated. AROLA is validated in simulation and on hardware using the RoboRacer platform, including deployment at the 2025 RoboRacer IV25 competition. Together, AROLA and Race Monitor demonstrate that modularity, transparent interfaces, and systematic evaluation can accelerate development and improve reproducibility in scaled autonomous racing.
