DPNet: 視覚ベースUAVナビゲーションのための効率的な行き止まり予測と回避
DPNet: Efficient Dead-End Prediction and Avoidance for Vision-Based UAV Navigation
RGB-D入力から行き止まりの相対距離と方位を予測する軽量ニューラルネットワークを提案し、軌道ライブラリを刈り込んでUAVが行き止まりを回避しながら滑らかに飛行できるようにした。実データでの追加学習なしで実世界に適用でき、50Hzでの高速再計画を実現する。
著者: Ruibin Zhang, Lun Pan, Zelong Xia, Jialiang Hou, Fei Gao
分類: cs.RO
原文アブストラクト
Vision-based Unmanned Aerial Vehicles (UAVs) often suffer from navigation failures in dead ends due to limited sensing accuracy and range. To address this challenge, this paper proposes a systematic solution for efficient dead-end prediction and avoidance. The proposed method introduces a lightweight neural network to predict the relative distance and bearing of potential dead ends within the current field of view using RGB-D inputs. These predictions prune a predefined, compact trajectory library, enabling the planner to proactively avoid dead ends while maintaining navigational smoothness. Notably, our approach transfers across real-world scenarios without manual annotation or fine-tuning on real-world data. The system achieves high-frequency replanning at 50 Hz onboard. Extensive simulation benchmarks demonstrate superior performance in success rate, flight time, and trajectory length, and real-world experiments further validate its effectiveness in complex scenarios.