# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm  
[PERSON]  
1 State Grid Hubel Information & Telecommunication Company, Wuhan 430048, China;  
[EMAIL_ADDRESS] (D.H.); jihbo724@126. Meanwhile, the UAV logistics market is experiencing rapid growth, with significant expansion across global regions. According to recent data [12], the global UAV logistics market is expected to reach 2.151 billion by 2025, with an annual growth rate of 21.1%. Regionally, North America is projected to hold the largest market share at approximately 40.3%, while the Asia-Pacific region is experiencing the fastest growth, particularly in China, which is expected to capture a significant share by 2030. These trends indicate that, as technology advances and demand increases, UAV logistics will become a crucial component of the global transportation sector. Despite the significant potential of UAVs in the IoT, their limited endurance and communication range still constrain their application in large-scale inspections. Additionally, the computational and storage limitations of UAVs hinder their performance in large-scale data processing. To overcome these challenges, scholars have proposed new collaborative models that integrate UAVs with ground vehicles [13; 14; 15; 16; 17]. This model combines the aerial flexibility of UAVs with the ground stability of vehicles, significantly extending the operational range and enhancing data processing capabilities. The collaboration between UAVs and vehicles not only addresses limitations in endurance and communication but also improves data processing efficiency and depth, providing new solutions for the widespread application of UAV technology across various scenarios. However, in practical applications, complex weather conditions can significantly disrupt the collaborative operation between UAVs and vehicles. Adverse weather factors, such as strong winds, precipitation, or low temperatures, reduce the flight speed and stability of UAVs, leading to longer task completion times. At the same time, harsh weather can destabilize ground traffic, causing issues like water accumulation, ice, or poor visibility, which slow down vehicle speed and further delay task execution. In time-sensitive scenarios, such as emergency rescue or logistics delivery, these delays not only hinder the timely delivery of critical goods but also risk missing essential response windows, potentially compromising public safety. Therefore, addressing the dynamic challenges posed by weather, optimizing path planning and scheduling strategies, and ensuring efficient collaboration between UAVs and ground vehicles to meet task deadlines are crucial issues that must be resolved. To address these challenges, this paper introduces a novel problem, termed the \"Dynamic Vehicle-UAV Collaborative Path Planning and Task Scheduling Problem\". Compared to traditional path planning problems, our approach is more complex because it not only focuses on optimizing the path but also ensures efficient collaboration between vehicles and UAVs, as well as dynamically adjusting task scheduling and allocation in an ever-changing environment. The model for this problem draws inspiration from the Dynamic Vehicle Routing Problem (DVRP) [18; 19; 20; 21; 22], which traditionally aims to minimize the travel distance or time for vehicles. However, the DVRP model does not account for the collaboration between vehicles and UAVs or the real-time scheduling of tasks in dynamic environments. In our model, UAV-vehicle collaboration involves not only path planning but also the efficient scheduling and allocation of tasks within a dynamic environment. First, appropriate collaborative nodes must be selected, which are key locations where vehicles interact with UAVs. These nodes serve as stops where vehicles launch or control UAV tasks. Second, the challenge lies in dynamically adjusting the paths of both vehicles and UAVs and reallocating tasks as task demands evolve, ensuring optimal resource utilization and timely responses.