Knowledge-Aware Path Planning for UAV Parcel Delivery and Traffic Monitoring

Unmanned aerial vehicles (UAVs) are transforming urban mobility, logistics, and traffic monitoring. Among their diverse applications, parcel delivery and road monitoring have emerged as particularly influential. While delivery UAVs aim to minimize response time and optimize routing, monitoring UAVs seek to maximize spatiotemporal coverage of transportation networks. This paper presents a unified decision-making framework that enables UAVs to jointly perform delivery and monitoring tasks in a coordinated manner. The urban environment is represented as a graph-based road network where each segment's importance value increases over time and is refreshed upon UAV visits. To address this problem, we design a knowledge-aware rolling horizon planning framework that operates in a fully distributed manner, allowing each UAV to re-plan its trajectory iteratively based on local state and shared global knowledge. This design ensures scalability and practical deployability. Simulations on real-world road networks show that our method achieves a superior balance between delivery efficiency and monitoring coverage compared to existing baselines.