氷海域における船舶と氷の相互作用予測を用いた自律航行
Autonomous Navigation in Ice-Covered Waters with Learned Predictions on Ship-Ice Interactions
船舶の動きに応じて動く氷の粗い動態を占有推定で予測する深層学習モデルを提案し、予測結果をキャッシュしてグラフ探索プランナに組み込むことで、氷との衝突を大幅に減らすリアルタイム自律航行を実現した。
著者: Ninghan Zhong, Alessandro Potenza, Stephen L. Smith
分類: cs.RO
原文アブストラクト
Autonomous navigation in ice-covered waters poses significant challenges due to the frequent lack of viable collision-free trajectories. When complete obstacle avoidance is infeasible, it becomes imperative for the navigation strategy to minimize collisions. Additionally, the dynamic nature of ice, which moves in response to ship maneuvers, complicates the path planning process. To address these challenges, we propose a novel deep learning model to estimate the coarse dynamics of ice movements triggered by ship actions through occupancy estimation. To ensure real-time applicability, we propose a novel approach that caches intermediate prediction results and seamlessly integrates the predictive model into a graph search planner. We evaluate the proposed planner both in simulation and in a physical testbed against existing approaches and show that our planner significantly reduces collisions with ice when compared to the state-of-the-art. Codes and demos of this work are available at https://github.com/IvanIZ/predictive-asv-planner.
