In this paper, we present Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical planning approach for multi-robot coordination in real-world environments with significant inter-robot spatial and temporal dependencies. At its core, STALC consists of a multi-robot graph-based planner which combines a topological graph with a novel, computationally efficient mixed-integer programming formulation to generate highly-coupled multi-robot plans in seconds. To enable autonomous planning across different spatial and temporal scales, we construct our graphs so that they capture connectivity between free-space regions and other problem-specific features, such as traversability or risk. We then use receding-horizon planners to achieve local collision avoidance and formation control. To evaluate our approach, we consider a multi-robot reconnaissance scenario where robots must autonomously coordinate to navigate through an environment while minimizing the risk of detection by observers. Through simulation-based experiments, we show that our approach is able to scale to address complex multi-robot planning scenarios. Through hardware experiments, we demonstrate our ability to generate graphs from real-world data and successfully plan across the entire hierarchy to achieve shared objectives.

翻译：本文提出了一种分层拓扑自主长程协调方法（STALC），这是一种针对具有显著机器人间时空依赖性的真实环境中多机器人协调的分层规划方法。其核心在于，STALC包含一个基于图的多机器人规划器，该规划器将拓扑图与一种新颖、计算高效的混合整数规划公式相结合，可在数秒内生成高度耦合的多机器人规划。为实现在不同时空尺度上的自主规划，我们构建了能够捕捉自由空间区域之间连通性以及可遍历性或风险等特定问题特征的拓扑图。随后，我们采用滚动时域规划器实现局部避碰和编队控制。为评估所提方法，我们考虑了一种多机器人侦察场景：机器人需自主协调穿越环境，同时最小化被观测者探测的风险。通过仿真实验表明，该方法能够有效扩展以处理复杂的多机器人规划场景。硬件实验进一步验证了我们从真实数据生成拓扑图的能力，并成功实现了跨整个层级的规划以达成共享目标。