未知環境におけるマルチロボットの知覚融合による効率的な協調ナビゲーション
Efficient Collaborative Navigation through Perception Fusion for Multi-Robots in Unknown Environments
未知環境で複数ロボットが協調して効率的にナビゲーションするため、グラフアテンションと情報利得を組み合わせた計画手法を提案し、シミュレーションと実機で有効性を示した。
著者: Qingquan Lin, Weining Lu, Litong Meng, Chenxi Li, Bin Liang
分類: cs.RO
原文アブストラクト
For tasks conducted in unknown environments with efficiency requirements, real-time navigation of multi-robot systems remains challenging due to unfamiliarity with surroundings.In this paper, we propose a novel multi-robot collaborative planning method that leverages the perception of different robots to intelligently select search directions and improve planning efficiency. Specifically, a foundational planner is employed to ensure reliable exploration towards targets in unknown environments and we introduce Graph Attention Architecture with Information Gain Weight(GIWT) to synthesizes the information from the target robot and its teammates to facilitate effective navigation around obstacles.In GIWT, after regionally encoding the relative positions of the robots along with their perceptual features, we compute the shared attention scores and incorporate the information gain obtained from neighboring robots as a supplementary weight. We design a corresponding expert data generation scheme to simulate real-world decision-making conditions for network training. Simulation experiments and real robot tests demonstrates that the proposed method significantly improves efficiency and enables collaborative planning for multiple robots. Our method achieves approximately 82% accuracy on the expert dataset and reduces the average path length by about 8% and 6% across two types of tasks compared to the fundamental planner in ROS tests, and a path length reduction of over 6% in real-world experiments.
