Perception is necessary for autonomous navigation in an unknown area crowded with obstacles. It's challenging for a robot to navigate safely without any sensors that can sense the environment, resulting in a $\textit{blind}$ robot, and becomes more difficult when comes to a group of robots. However, it could be costly to equip all robots with expensive perception or SLAM systems. In this paper, we propose a novel system named $\textbf{ColAG}$, to solve the problem of autonomous navigation for a group of $\textit{blind}$ UGVs by introducing cooperation with one UAV, which is the only robot that has full perception capabilities in the group. The UAV uses SLAM for its odometry and mapping while sharing this information with UGVs via limited relative pose estimation. The UGVs plan their trajectories in the received map and predict possible failures caused by the uncertainty of its wheel odometry and unknown risky areas. The UAV dynamically schedules waypoints to prevent UGVs from collisions, formulated as a Vehicle Routing Problem with Time Windows to optimize the UAV's trajectories and minimize time when UGVs have to wait to guarantee safety. We validate our system through extensive simulation with up to 7 UGVs and real-world experiments with 3 UGVs.

翻译：感知能力对于机器人在未知障碍物密集区域进行自主导航至关重要。缺乏环境感知传感器的"盲"机器人难以实现安全导航，而多机器人系统面临更大挑战。然而，为所有机器人配备昂贵的感知或SLAM系统成本高昂。本文提出名为**ColAG**的新型系统，通过引入一架具备完整感知能力的无人机与多台"盲"无人地面车辆协同，解决其自主导航难题。无人机利用SLAM实现自身里程计与地图构建，并通过有限相对位姿估计将环境信息共享给无人地面车辆。后者基于接收的地图规划轨迹，并预测轮式里程计不确定性及未知危险区域可能引发的故障。无人机通过动态调度航点防止无人地面车辆碰撞——该问题被建模为带时间窗的车辆路径问题，以优化无人机轨迹并最小化无人地面车辆为保证安全所需的等待时间。最终通过含7台无人地面车辆的仿真实验及3台无人地面车辆的真实场景实验验证系统有效性。