# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 2 Related Works  
Multi-UAV regional coverage trajectory planning is an important research direction in the field of UAVs. The academic community proposed many methods and algorithms, which can be mainly divided into intelligent optimization algorithms, neural network algorithms, and reinforcement learning algorithms. Intelligent optimization algorithms are widely used in practical multi-UAV regional coverage trajectory planning scenarios. [PERSON], [PERSON], et al. [17] demonstrated the advantages of swarm algorithms in speeding up the path-planning process and improving the efficiency of UAV path-planning. [PERSON] et al. [18] proposed an improved IBA algorithm with a shorter optimization time and higher-quality optimal solution. [PERSON] et al. [19] introduced a new phase-encoded fruit fly optimization algorithm (FOA) to explore the optimal flight path of UAVs in complex three-dimensional terrain scenes. [PERSON] et al. [20] proposed a path-planning method based on the improved ant colony system algorithm (IACS) for dense target group models to minimize the total time of UAV mission execution. [PERSON] et al. [21] introduced an improved artificial bee colony algorithm (IABC) based on multi-strategy synthesis to maximize the quality of flight paths. [PERSON] et al. [22] proposed a hierarchical recursive multi-agent genetic algorithm (HR-MAGA) with powerful global optimization capabilities and can provide adaptive path planning in real-time under dynamic environments. [PERSON] et al. [23] verified the effectiveness and superiority of genetic algorithms in UAV coverage path planning in three-dimensional environments. [PERSON] et al. [24] proposed an adaptive, non-dominated sorting genetic algorithm (Adaptive NSGA-II) to find the optimal path-planning solution. [PERSON] et al. [25] used particle swarm optimization (PSO) to achieve multi-UAV coverage tasks while minimizing the total flight distance and maximizing the coverage rate. Neural networks can learn from massive data samples to train models and adjust the paths in real time according to the scene, making them more adaptable to the natural environment. [PERSON] et al. [26] proposed an algorithm that limits the new gene generation area and dynamically adjusts the evolution operator to improve the algorithm's local search ability and search efficiency. [PERSON], [PERSON] [27] introduced a method for autonomous UAV (UAV) path navigation and obstacle avoidance using deep neural networks. [PERSON] et al. [28] trained a neural network to learn and predict the best path in real time in an unknown environment. [PERSON], [PERSON], et al. [29] proposed a UAV obstacle avoidance method based on a neural network and an interfering fluid power system to achieve real-time obstacle avoidance. [PERSON], [PERSON], et al. [30] used the visual input of the UAV's onboard camera to achieve autonomous navigation of the UAV on a predetermined path. [PERSON] et al. [31] proposed using an iterative adaptive dynamic programming algorithm to obtain an iterative tracking control law to enhance the performance of multi-UAV tracking. [PERSON] et al. [32] obtained information from the environment based on the DQN algorithm and obtained the optimal trajectory planning strategy through learning, thereby achieving efficient regional coverage. [PERSON] et al. [33] explored a method based on the cerebellum model arithmetic computer to optimize the path planning of multiple UAVs to improve flight efficiency and coverage. [PERSON] et al. [34] combined the advantages of genetic algorithms and neural networks to adjust and optimize the paths of multiple UAVs adaptively. Reinforcement learning is learned through interaction between agents and the environment, and it adjusts its strategy according to the reward signals obtained. It has tremendous application potential in multi-UAV area coverage trajectory planning. [PERSON], [PERSON], et al.