DUGM-R: 不確実性を考慮した動的グリッドマッピングとリスクトリガ型リカバリによる学習ベース局所ナビゲーション
DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation
混雑環境での局所ナビゲーション向けに、障害物の動きとその不確実性を統合した動的グリッドマップと、衝突リスクを予測してリカバリ方策を起動する学習フレームワークを提案し、シミュレーションと実機TurtleBot3で有効性を示した。
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著者: Haoyun Feng, Adrian Rubio-Solis, Zhaodong Guo, George Mylonas
分類: cs.RO
原文アブストラクト
Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may persist after nominal policy training. We present a risk-aware reinforcement-learning framework that addresses these two issues through an uncertainty-aware Dynamic Uncertainty Grid Map (DUGM) and a modular post-training recovery mechanism. DUGM combines local occupancy, estimated obstacle motion, and motion-estimation uncertainty in a robot-centric representation. After the nominal policy is frozen, a finite-horizon Risk Value Function (RVF) is trained from nominal rollouts and used to trigger a dedicated recovery policy when continued nominal execution is predicted to be collision-prone. Experiments in a held-out NVIDIA Isaac Sim clinical-logistics benchmark show that uncertainty-aware dynamic representation improves nominal navigation over static and deterministic alternatives, while the recovery mechanism further mitigates residual collision-prone behaviour. The complete framework is also deployed directly on a TurtleBot3 without policy fine-tuning, retraining, or site-specific adaptation, retaining the performance trend observed in simulation. These results indicate that uncertainty-aware dynamic representation and post-training recovery provide complementary mechanisms for improving learned local navigation.