用逻辑约束优化无人机送血路线，确保准时避障
Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance
- 用时序逻辑形式化时间与空间要求，统一建模任务目标
- 通过凸可行集方法实现3D建筑避障，生成无碰撞轨迹
- 方法可高效求解，适合城市医疗无人机配送场景
本文研究城市环境中无人机执行时效性医疗配送的轨迹优化问题。考虑具三自由度动力学特性的单架无人机，需向多个具有预设时间窗和优先级的医院运送血液包。任务目标通过信号时序逻辑（STL）形式化，以精确表达时空约束。为保障安全，城市建筑被建模为三维凸障碍物，利用凸可行集（CFS）方法实现避障。整个规划问题——结合无人机动力学、STL满足性及避障——被构建成一个凸优化问题，保证可计算性，并可通过标准凸规划技术高效求解。仿真结果表明，该方法能生成动态可行、无碰撞且满足时间目标的飞行轨迹，为自主无人机医疗物流提供了一种可扩展、可靠的解决方案。
原文摘要 · Abstract (English)
This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3 degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem-combining UAV dynamics, STL satisfaction, and collision avoidance-is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。