FinsSim: 水中ロボット学習のための現実整合型統合シミュレーションプラットフォーム
FinsSim: A Reality-Aligned Integrated Simulation Platform for Underwater Robot Learning
水中ロボットのSim-to-Real学習向けに、高忠実度流体力学・学習インターフェース・低コスト高精度自己位置推定・推力モデルを統合したシミュレーションプラットフォームを構築し、実機実験で制御方策の転移を検証した。
詳しい要約
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著者: Yu Zhang, Yuanmingqing Song, Xiangyun Rao, Pangkit Fong, Kunhao Zhang, Chongrong Fang, Jianping He
分類: cs.RO, eess.SY
原文アブストラクト
Underwater robot learning relies on simulators that integrate high-fidelity hydrodynamics, convenient learning interfaces, and a credible transition to real scenarios. In this work, we present FinsSim, a reality-aligned integrated simulation platform for Sim-to-Real underwater robot learning. FinsSim first constructs high-fidelity simulation with selectable backends to adapt to diverse requirements. To facilitate underwater robot research, it further offers standard control baselines, alongside with unified robot learning workflows. For reliable Sim-to-Real transfer, FinsSim adopts a multi-sensor fusion scheme to provide low-cost yet precise localization. Moreover, it implements calibrated thruster-hydrodynamics models and a constrained wrench allocation algorithm. Bridging these modules by ROS~2, FinsSim establishes a complete Sim-to-Real transfer pipeline. Through matched simulations and experiments, it is demonstrated that reliable Sim-to-Real transfer of underwater robot control policies can be achieved with the FinsSim framework. Separate ablation studies also validate that the modules of FinsSim can address the pivotal issues of underwater Sim-to-Real from different aspects. Overall, this work aims to bridge the gap between theoretical research and practical applications, ultimately driving advancements in the field of underwater robotics.
