全向传感器放置：一项大规模计算研究与新型混合加速精炼启发式方法
Omnidirectional Sensor Placement: A Large-Scale Computational Study and Novel Hybrid Accelerated-Refinement Heuristics
AI总结：
针对连续二维环境中的全向传感器放置问题，本文通过大规模计算实验比较凸划分与采样方法，并提出混合加速精炼启发式HAR，以更少传感器和更优运行时实现覆盖要求。
摘要：
本文研究全向传感器放置问题（OSPP），该问题涉及在连续二维环境中放置静态传感器，以在满足用户定义覆盖要求的同时最小化传感器数量。该问题的动机来自移动机器人应用，尤其是优化基于可见性的路径规划任务，例如环境巡检、目标搜索和区域巡逻。我们关注全向可见性模型，这类模型消除了传感器朝向约束，同时仍与LiDAR、360度相机和多传感器阵列等现实传感技术相关。文中考虑三种关键模型：无限可见性、用于反映物理或应用特定约束的有限距离可见性，以及用于考虑机器人中传感器放置不确定性的定位不确定性可见性。我们的第一项贡献是一项大规模计算研究，比较用于OSPP的经典凸划分启发式方法和基于采样的启发式方法，分析它们在运行时效率与解质量之间的权衡。第二项贡献是提出一类新的混合加速精炼（HAR）启发式方法，这些方法组合并精炼多种传感器放置方法的输出，同时纳入预处理技术以加速精炼。结果表明，HAR启发式方法显著优于传统方法，实现了最低传感器数量，并改善了基于采样方法的运行时。此外，我们将一种特定HAR启发式方法调整用于定位不确定性可见性模型，结果表明它能在小到中等定位不确定性下实现所需覆盖。未来工作可将HAR应用于基于可见性的路径规划任务，或探索新型传感器放置方法，以在不确定性下实现形式化覆盖保证。
英文摘要：
This paper studies the omnidirectional sensor-placement problem (OSPP), which involves placing static sensors in a continuous 2D environment to achieve a user-defined coverage requirement while minimizing sensor count. The problem is motivated by applications in mobile robotics, particularly for optimizing visibility-based route planning tasks such as environment inspection, target search, and region patrolling. We focus on omnidirectional visibility models, which eliminate sensor orientation constraints while remaining relevant to real-world sensing technologies like LiDAR, 360-degree cameras, and multi-sensor arrays. Three key models are considered: unlimited visibility, limited-range visibility to reflect physical or application-specific constraints, and localization-uncertainty visibility to account for sensor placement uncertainty in robotics. Our first contribution is a large-scale computational study comparing classical convex-partitioning and sampling-based heuristics for the OSPP, analyzing their trade-off between runtime efficiency and solution quality. Our second contribution is a new class of hybrid accelerated-refinement (HAR) heuristics, which combine and refine outputs from multiple sensor-placement methods while incorporating preprocessing techniques to accelerate refinement. Results demonstrate that HAR heuristics significantly outperform traditional methods, achieving the lowest sensor counts and improving the runtime of sampling-based approaches. Additionally, we adapt a specific HAR heuristic to the localization-uncertainty visibility model, showing that it achieves the required coverage for small to moderate localization uncertainty. Future work may apply HAR to visibility-based route planning tasks or explore novel sensor-placement approaches to achieve formal coverage guarantees under uncertainty.
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