# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 2 Related Work
[44] introduced an enhanced half-random Q-learning algorithm that adeptly circumvents the pitfalls of local optimization. However, this algorithm falls short in addressing task allocation within uncertain contexts. [PERSON] et al. [45] addressed the path planning predicament by translating it into a discrete partially observable Markov decision process, subsequently employing deep reinforcement learning to derive optimal UAV control strategies. [PERSON] et al. [46] synergistically combined the double screening sampling method with the depth deterministic strategy gradient algorithm, culminating in the REL-DDPG algorithm, notable for its heightened convergence rate. The evolution of neural networks and reinforcement learning has notably shaped strategies to overcome these challenges. Nevertheless, these neural network-based methodologies often demand substantial quantities of training data and time, potentially impeding their suitability for dynamic scenarios necessitating rapid decision making. Distributed algorithms have found extensive application in task scenarios characterized by emergent situations. In the context of optimization problems within dynamic scenarios, [PERSON] et al. [47] proposed a cooperative co-evolutionary algorithm that dynamically groups decision variables to effectively address MOPs with changing decision variables. This approach excels in terms of diversity, convergence, and the spread of solutions on most benchmark optimization problems. [PERSON] et al. [48] introduced a multi-objective genetic algorithm (MOGAMR) specifically tailored to address real-time task reassignment predicaments. This approach generates a set of feasible solutions within stipulated timeframes by leveraging previous solution information when encountering new tasks. However, this process is time-intensive. [PERSON] et al. [49] devised a distributed method aimed at handling dynamic events arising during the original scheduling process. This method efficiently delivers conflict-free solutions while minimizing data exchange and runtime. In the realm of task assignment, which entails the generation of novel tasks, [PERSON] et al. [50] extended the consensus-based bundle algorithm (CBBA). By reconfiguring the final task allocation for each agent, this algorithm mitigates the magnitude of reprogramming required. Despite the rapid execution and adaptability of distributed algorithms in dynamic task assignment scenarios, the solutions derived from these approaches frequently fall short of optimality. The integration of online dynamic allocation capabilities into a multi-UAV system offers substantial benefits, notably bolstering the fault tolerance and overall reliability of the UAV infrastructure. Pioneering this direction, [PERSON] et al. [51] introduced the application of evolutionary algorithms to task assignment and harnessed the contract network protocol method for task reassignment in emergency contexts. Their work thoughtfully combines the advantages of high-quality solutions from centralized algorithms with the rapid responsiveness inherent to distributed algorithms. Building upon this foundation, this paper introduces a novel multi-UAV multi-target cooperative reconnaissance scheme to tackle the challenge of obtaining high-quality solutions in dynamic reconnaissance operations. The proposed approach consists of two key components. Firstly, a heuristic algorithm is utilized, leveraging global information to perform pre-allocation of reconnaissance tasks, thereby obtaining superior pre-allocation solutions. Subsequently, an enhanced distributed algorithm, grounded on the auction mechanism, is employed to dynamically adjust the allocation scheme in response to real-time environmental changes during task execution. This improved distributed algorithm aims to minimize the total flight distance of UAVs while yielding higher-quality solutions as compared to conventional distributed algorithms.