# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 1 Introduction
The distributed method boasts advantages in terms of timeliness, flexibility, and robustness owing to its decentralized nature. Nonetheless, certain factors, such as the limited computing power of UAVs and the inherent limitations of distributed algorithms in achieving global optimization, may result in suboptimal allocation schemes [24]. This study presents an innovative cooperative reconnaissance scheme involving multiple UAVs and targets, aimed at addressing the challenge of attaining a solution with smaller flight distance and higher reconnaissance benefits during dynamic reconnaissance operations. Different from other schemes, the proposed method comprises two pivotal components. Firstly, a centralized heuristic algorithm is employed to obtain a static allocation solution because the centralized algorithm can use the global information, and can obtain a better solution than the distributed algorithm. Subsequently, an enhanced distributed algorithm, based on the auction mechanism, is utilized for dynamic adaptation of the allocation scheme in real time, compensating for the shortcomings of centralized algorithms in handling dynamic scenarios. The pre-allocation of solutions based on global information can be represented as a constrained multi-objective problem (CMOP). A variety of constrained multi-objective algorithms (CMOEAs) have been developed to address CMOPs [25]. An essential aspect of handling CMOPs lies in effectively balancing conflicts between constraints and objectives during the evolutionary process. CMOEAs that progressively adjust the weights of constraints and objectives [26; 27] may encounter challenges in escaping large unfeasible regions. To overcome these issues, several CMOEAs [28; 29] employing two-stage optimization have been proposed to approach the unconstrained Pareto frontier (UPF)and mitigate the impact of large unfeasible regions. However, these approaches may not perform optimally when dealing with CMOPs characterized by small and discrete feasible regions. Moreover, CMOEAs [30,31] based on dual population optimization have been developed to tackle these problems, but they may lack effectiveness in solving CMOPs with optimal feasible regions situated far from the UPF, as the auxiliary population may not adequately maintain and utilize promising infeasible solutions in the later evolutionary stages. To address these challenges, a novel multitasking constrained multi-objective optimization framework (MTCMO) has been proposed in the literature [32]. This framework generates dynamic auxiliary tasks through knowledge transfer, aiding in the resolution of complex CMOPs (primary task), while allowing promising infeasible solutions to persistently contribute to the later evolutionary stages. The MTCMO framework has demonstrated robust performance in solving CMOPs. Hence, in this study, we adopt this framework to obtain pre-allocation solutions in the first stage. The primary contribution of this work is to introduce and investigate a dynamic scenario model that considers UAV damage and the emergence of new reconnaissance areas. Additionally, an innovative collaborative multi-target and multi-UAV reconnaissance scheme is proposed, which utilizes heuristic algorithms and distributed computing as two phases of our algorithm to address the problem. Finally, we conduct simulations to validate the effectiveness of the algorithm. The rest of this paper is as follows. Section 2 introduces the related work on multi-UAV multi-target cooperative reconnaissance task allocation. In Section 3, the research problem is described and modeled. Section 4 describes the proposed multi-UAV multi-target cooperative reconnaissance scheme in detail. In Section 5, the effectiveness of the algorithm is verified by simulation experiments. Finally, conclusions are given in Section 6.