面向无人机辅助搜救任务的基于量子的k覆盖优化
Quantum-Based k-Coverage Optimization for UAV-Aided Search and Rescue Missions
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中文总结 AI 辅助
针对无人机辅助搜救的航点选择问题，提出基于量子近似优化算法的QUBO方法，在量子硬件上实现了高覆盖率且路径更短的航点优化，优于传统基线。
中文摘要 AI 辅助
在大规模灾害场景中，快速定位失踪人员是搜救（SAR）行动的关键挑战。配备射频（RF）接收器的无人机（UAV）可通过在空间分布的传感位置收集移动设备发射的信号，为基于RF的定位提供支持。本文解决由此产生的航点选择问题：确定一组最小的无人机航点，使其对每个潜在目标位置提供至少3倍覆盖。我们将该任务表述为扩展的k覆盖问题，该问题独立定义了无人机可导航区域和目标区域，并推导了具有充分惩罚条件的精确惩罚二次无约束二元优化（QUBO）公式，该公式可保持可行性和最小航点基数。将QUBO映射为伊辛形式的成本哈密顿量，并在无噪声模拟器和IBM的127量子比特Eagle处理器上使用量子近似优化算法（QAOA）进行评估。在测试的模拟器实例中，QAOA恢复了已知的最小基数解；在矩形硬件测试案例中，平均3倍覆盖率超过95%；在校园规模评估中，10次硬件执行实现了99.3%的平均3倍覆盖率和90%的可行运行率，且最短可行飞行路径比基于确定性网格的基线短达37.0%。还提供了与经典优化和基于学习的基线的额外比较，以及对更大生成实例的计算和量子资源分析。这些结果确立了用于基于RF的SAR航点选择的精确QUBO表示，并表征了其在当前门模型量子硬件上的实现。
英文摘要
In large-scale disaster scenarios, rapid localization of missing persons is a critical challenge for search-and-rescue (SAR) operations. Unmanned aerial vehicles (UAVs) equipped with radio frequency (RF) receivers can support RF-based localization by collecting signals emitted from mobile devices at spatially distributed sensing locations. This paper addresses the resulting waypoint-selection problem: determining a minimum set of UAV waypoints that provides at least threefold coverage of every potential target location. We formulate this task as an extended k-coverage problem that independently defines the UAV-navigable and target regions, and derive an exact-penalty quadratic unconstrained binary optimization (QUBO) formulation with a sufficient penalty condition that preserves feasibility and minimum waypoint cardinality. The QUBO is mapped to an Ising-form cost Hamiltonian and evaluated using the quantum approximate optimization algorithm (QAOA) on both a noise-free simulator and IBM's 127-qubit Eagle processor. On the tested simulator instances, QAOA recovers the known minimum-cardinality solutions. Across the rectangular hardware test cases, the mean 3-coverage ratio exceeded 95%. In the campus-scale evaluation, ten hardware executions achieved 99.3% mean 3-coverage with a 90% feasible-run rate, while the shortest feasible flight path was up to 37.0% shorter than those of the deterministic grid-based baselines. Additional comparisons with classical optimization and learning-based baselines are provided, together with computational and quantum-resource analyses for larger generated instances. These results establish an exact QUBO representation for RF-based SAR waypoint selection and characterize its implementation on current gate-based quantum hardware.
发表机构
- Yonsei University（延世大学）
- University of Waterloo（滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。
