HOPHY：用于越野路径与任务规划的层次超图表示
HOPHY: A Hierarchical Hypergraph Representation for Off-Road Path and Mission Planning
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中文总结 AI 辅助
HOPHY提出一种层次超图地形表示，实现高效越野路径与任务规划，在公里级地图上达到100%规划成功率且代价偏差极小，计算量较像素A*降低79倍。
中文摘要 AI 辅助
面向灾害响应、搜索救援及战术无人地面车辆（UGV）任务的任务级自主性，要求在地形条件、智能体类型和目标变化时，能够重复进行路径与任务规划。像素网格搜索在重复的公里级查询中代价高昂，而语义抽象在条件变化时必须维持有效的代价和连通性。我们提出HOPHY（基于超图的层次化越野规划），一种可复用的层次化地形表示，将地图规模的地形组织为几何连通的语义区域（GSNodes）、保持连通性的关键区域（Coarse Regions），以及针对地形、智能体和天气上下文的类型化超边。超边交集选择受影响的区域和关联边以进行状态更新，而无需重建层次结构。在跨越公里级区域的真实越野地图上，HOPHY实现了100%的规划成功率，且与最优解（像素A*）相比，中位代价偏差小于0.01%，同时查询和重新规划延迟显著低于所评估的像素和抽象基线。应用于多机器人任务分配（MRTA）问题时，这些优势使总计算量比像素A*减少79倍，比最快的抽象基线减少7.2倍，且任务完成时间与像素A*相当。最后，我们在物理Clearpath Jackal平台上演示了HOPHY，成功执行了跨越混合表面户外地形的1.5公里、八任务任务，以及由障碍触发的重新规划路线。
英文摘要
Mission-level autonomy for disaster response, search and rescue, and tactical UGV operations requires repeated path and mission planning as terrain conditions, agent types, and objectives change. Pixel-grid search is costly for repeated kilometer-scale queries, while semantic abstractions must maintain valid costs and connectivity as conditions change. We present HOPHY (Hierarchical Off-Road Planning using Hypergraphs), a reusable hierarchical terrain representation that organizes map-scale terrain into geometrically connected semantic regions (GSNodes), connectivity-preserving critical regions (Coarse Regions), and typed hyperedges for terrain, agent, and weather context. Hyperedge intersections select affected regions and incident edges for state updates without rebuilding the hierarchy. Across real off-road maps spanning kilometer-scale areas, HOPHY achieves 100% planning success and less than 0.01% median cost deviation from the oracle (pixel A*), with substantially lower query and replanning latency than the evaluated pixel and abstraction baselines. Applied to a multi-robot task-allocation (MRTA) problem, these gains reduce total computation by 79x over pixel A* and 7.2x over the fastest abstraction baseline, with mission makespan comparable to pixel A*. Finally, we demonstrate HOPHY on a physical Clearpath Jackal that successfully executes a 1.5-km, eight-task mission across mixed-surface outdoor terrain and a blockage-triggered replanned route.
发表机构
- University at Buffalo（布法罗大学）
机构由 AI 辅助整理，请以论文原文为准。
