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.

翻译：在具有效率要求的未知环境任务中，由于对环境的不熟悉，多机器人系统的实时导航仍然具有挑战性。本文提出了一种新颖的多机器人协同规划方法，该方法利用不同机器人的感知信息智能选择搜索方向，从而提高规划效率。具体而言，我们采用一个基础规划器来确保在未知环境中朝向目标的可靠探索，并引入了带有信息增益权重的图注意力架构（GIWT），以综合来自目标机器人及其队友的信息，从而促进在障碍物周围的有效导航。在GIWT中，在对机器人相对位置及其感知特征进行区域编码后，我们计算共享注意力分数，并将从邻近机器人获得的信息增益作为补充权重纳入其中。我们设计了一个相应的专家数据生成方案，以模拟网络训练所需的真实世界决策条件。仿真实验和真实机器人测试表明，所提方法显著提高了效率，并实现了多机器人的协同规划。我们的方法在专家数据集上达到了约82%的准确率，在ROS测试中，与基础规划器相比，在两类任务上平均路径长度分别减少了约8%和6%，在真实世界实验中路径长度减少了超过6%。