MSC: 不確かさを考慮したNMPCと強化学習実行層による多段階キャスタ対応制御フレームワーク
MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer
キャスタの向きずれに強いAMR制御のため、予測不確かさを考慮したNMPCと強化学習による実行層を組み合わせ、指令速度を接触状態に適応した車輪速度へ変換する階層型フレームワークを提案した。
詳しい要約
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著者: Jinho Park, Jeongtae Lee, Jeonghyeok Lim, Keun Ha Choi, Kyung-Soo Kim
分類: cs.RO, cs.LG
原文アブストラクト
Freely swiveling casters are widely adopted in industrial AMRs/SDVs for their mechanical simplicity and load capacity. However, caster misalignment during low-speed or high-curvature maneuvers induces strong contact nonlinearities, widening the planning-execution gap between commanded $(v, ω)$ and realized motion. Existing caster-aware strategies are highly sensitive to state-estimation reliability, causing unstable execution in uncertain transition regimes. To address this, we present Multi-Stage Caster-aware Control (MSC), a planning-execution integrated hierarchical framework featuring two core mechanisms. First, an uncertainty-aware NMPC (CU-MPC) dynamically schedules caster-aware costs and input regularization using predictive uncertainty to suppress overly aggressive maneuvers. Second, a reinforcement learning-based execution layer estimates and predicts caster states under partial observability, transforming high-level velocity commands into physically executable wheel speeds adapted to current contact conditions. Validations in Isaac Sim and real robots demonstrate that MSC offers a clear comparative advantage over existing decoupled compensation approaches by significantly improving caster-state estimation accuracy, minimizing misalignment, and enhancing tracking robustness in caster-sensitive maneuvers.
