## 1 Introduction  
With the advancement of low-cost, miniaturized sensor component technology and high-performance processor technology, miniaturized UAVs have experienced rapid development and widespread application in recent years [1, 2]. Clustering, autonomy, intelligence, and systematization not only mark the forefront trends of UAV technology but also open up new fields and prospects for the practical application of UAVs [3]. Especially in domains such as forest fire prevention and monitoring [4], agricultural pest and disease surveillance [5], seismic disaster communication relay [6], battlefield intelligence reconnaissance [7], and target tracking in complex environments [8], intelligent unmanned clusters demonstrate their unique advantages. The core of unmanned cluster networks lies in the interconnection and intercommunication of information among numerous UAV nodes through autonomous distributed network structure [9, 10, 11]. Compared to traditional information transmission networks, unmanned cluster networks exhibit superior flexibility, maneuverability and adaptability, supporting rapid deployment and movement of UAV nodes, eliminating the reliance on ground-based stations, and providing robust information exchange guarantees for the execution of unmanned cluster missions [12]. One of the typical application scenarios for contemporary unmanned clusters is in terrestrial urban environments, which has characteristics such as dense high-rise buildings, crisscrossing streets and complex underground infrastructure [13]. When unmanned clusters carry out tasks in complex urban environments with high dynamics and strong confrontation, there will be some problems such as severe limitations in spectrum resources [14] and intense competition for network resources [15]. The finiteness of network resources and the instability of network links can greatly affect the success rate of unmanned cluster task execution [16]. In view of the above problems, this paper focuses on how to ensure the link stability and data transmission reliability of unmanned cluster networks in highly dynamic and complex urban environments. This paper aims to propose an innovative routing protocol from the network level to help unmanned cluster networks adapt to the optimal routing path in highly dynamic environments, so as to reduce network congestion, minimize communication delay, and improve the overall efficiency and reliability of the network. The network routing protocol is fundamental to ensuring efficient data communication among UAV nodes in dynamic and challenging environments. Over the years, a variety of routing protocols have been developed to address the unique demands of unmanned cluster networks, such as high mobility, limited resources, and dynamic topologies. For instance, an intelligent routing protocol for multi-UAV networks was proposed, in which routing decisions are dynamically adjusted to ensure a minimum number of hops to the destination and better link quality by employing the Q-learning technique, resulting in improved end-to-end delivery and packet delivery ratio compared to the standard ad hoc routing protocol [17]. Similarly, [PERSON] et al. introduced the FNTAR protocol, which makes routing decisions based on UAV future locations using trajectory, position, and motion data. This method reduces data loss in unstable links and dynamic topologies by forwarding messages to multiple optimal next-hop UAVs, outperforming the DTN geographic protocol in terms of latency and delivery ratio [18]. Moreover, a robust route discovery process based on the Penguins Search Optimization Algorithm (PeSOA) was proposed, designed to optimize routing paths by considering energy consumption and path connectivity, addressing challenges such as fast movement, packet loss, and poor communication in FANETs [19]. Although significant progress has been made in the design of routing protocols, challenges persist in adapting them to highly dynamic and complex urban environments.