# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 1 Introduction  
The utilization of intelligence is experiencing a notable surge, and with the increasing automation of robot design [1], UAVs have emerged as essential tools in the information battlefield [2]. Nonetheless, due to the intricacy and variability of reconnaissance tasks and the ever-increasing number of targets requiring reconnaissance, a single UAV is insufficient to meet these task demands [3]. In light of this, the integration of multiple UAVs, exhibiting mutual cooperation and complementary advantages, has gained significant traction. This paradigm of multi-UAV cooperation facilitates diverse reconnaissance missions, encompassing the reconnaissance of multiple target areas, thereby substantially enhancing the overall reconnaissance efficiency [4]. Within the realm of multi-UAV collaboration, the technology facilitating multi-UAV and multi-target cooperative reconnaissance assumes paramount importance as it leverages the cooperative advantages of employing multiple UAVs together [5]. This technology encompasses two vital components, namely task allocation and path planning. Task allocation strives to effectively orchestrate the entire mission by intelligently assigning each UAV with specific targets and tasks. On the other hand, path planning pertains to determining the optimal trajectory for each UAV, guiding them from their initial positions to the designated target locations. The primary objective ofpath planning is to discover an optimal route that enables more efficient executions of all assigned tasks [6]. The task allocation problem in the context of multi-UAV and multi-target cooperative reconnaissance represents a complex and NP-hard challenge. Presently, the prevailing models utilized to address this problem encompass the Traveling Salesman Problem (TSP) model [7] and its extended variations [8; 9]. These models require the UAVs to cover all designated target points during reconnaissance missions, rendering them well-suited for scenarios characterized by extended flight times. Notably, they offer the advantages of low computational complexity and robust scalability. However, the application of these models require the establishment of pertinent constraints aligned with the specific problem context. In tackling the multi-UAV cooperative reconnaissance task allocation problem, two primary approaches are commonly employed: centralized methods and distributed methods [10]. In the centralized method, each individual UAV transmits its state information to a central processing unit, which then consolidates the global information and formulates an optimal allocation scheme [11]. Noteworthy centralized methods include optimization techniques [12; 13], and metaheuristic methods [14; 15; 16]. These approaches demonstrate favorable attributes in terms of globality and solution quality, as they are capable of producing high-quality solutions. However, the centralized method does suffer from certain limitations, including low timeliness and robustness, primarily stemming from the requirement to integrate global information. As a consequence, it is most suitable for static problems, such as the pre-allocation of reconnaissance tasks. In practical reconnaissance operations, real-time challenges often arise, such as the emergence of new tasks or UAV damage, rendering centralized methods inadequate in meeting these real-time requirements [17]. Addressing the dynamic and real-time demands of practical reconnaissance operations has led to significant research focus on the distributed reconnaissance task allocation method. Unlike the centralized approach, the distributed method substantially reduces reliance on a central processing unit, fostering information sharing, negotiation, and decision making among the UAVs to formulate a reconnaissance task allocation scheme. Prominent distributed methods encompass the contract net protocol algorithm [18; 19; 20], and the distributed auction algorithm [21; 22; 23], both of which utilize a market auction mechanism as their core principle.