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：本文提出分层拓扑自主长程协调（STALC），一种面向真实环境中多机器人协调的分层规划方法，可处理机器人之间显著的时空依赖性。其核心在于基于多机器人图结构的规划器，将拓扑图与新型计算高效混合整数规划公式相结合，在数秒内生成高度耦合的多机器人规划方案。为实现跨不同时空尺度的自主规划，我们构建的图结构既能捕捉自由空间区域间的连通性，又能表征可穿越性、风险等特定问题特征。在此基础上，采用滚动时域规划器实现局部避碰与编队控制。为评估该方法，我们研究了多机器人侦察场景：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。仿真实验表明，该方法可扩展至复杂多机器人规划场景；硬件实验则验证了从真实数据生成图结构并完成全层级规划以实现共同目标的能力。