改进优化算法，让多无人机在城市中高效安全地筛查发热人群。
Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments
- 基于信任域的凸优化框架，融合平滑轨迹与避障约束
- 仿真显示轨迹最优性与计算效率显著优于传统方法
- 适合关注城市无人机巡检与公共卫生应用的研究者
在传染病暴发期间，快速检测城市人群中的异常体温对防控公共健康风险至关重要。多无人机热成像监测系统为实现快速、大范围且非侵入式的人体温度监控提供了可行方案。然而，在复杂城市环境中进行多无人机轨迹规划面临巨大挑战，包括碰撞规避、覆盖效率及飞行空间受限等问题。本文提出一种增强型信任域序列凸优化（TR-SCO）算法，用于多无人机执行热成像筛查任务时的最优轨迹规划。该算法在信任域框架内采用改进的凸优化形式，有效平衡轨迹平滑性、障碍物规避、高度约束与最大覆盖范围。仿真结果表明，相较于传统凸优化方法，本方法在轨迹最优性与计算效率方面均有显著提升。本研究为实现在城市区域实时热成像筛查的高效多无人机系统部署提供了关键洞见与实际贡献。相关源代码已开源：https://github.com/Cherry0302/Enhanced-TR-SCO。
原文摘要 · Abstract (English)
The rapid detection of abnormal body temperatures in urban populations is essential for managing public health risks, especially during outbreaks of infectious diseases. Multi-drone thermal screening systems offer promising solutions for fast, large-scale, and non-intrusive human temperature monitoring. However, trajectory planning for multiple drones in complex urban environments poses significant challenges, including collision avoidance, coverage efficiency, and constrained flight environments. In this study, we propose an enhanced trust region sequential convex optimization (TR-SCO) algorithm for optimal trajectory planning of multiple drones performing thermal screening tasks. Our improved algorithm integrates a refined convex optimization formulation within a trust region framework, effectively balancing trajectory smoothness, obstacle avoidance, altitude constraints, and maximum screening coverage. Simulation results demonstrate that our approach significantly improves trajectory optimality and computational efficiency compared to conventional convex optimization methods. This research provides critical insights and practical contributions toward deploying efficient multi-drone systems for real-time thermal screening in urban areas. For reader who are interested in our research, we release our source code at https://github.com/Cherry0302/Enhanced-TR-SCO.
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