# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 2 Related Works
[35] proposed a deep reinforcement learning combined with global and local map information Unmanned Aerial Vehicle (UAV) path-planning method, which enables UAVs to make movement decisions in the environment and balance mission objectives and navigation constraints. [PERSON], [PERSON], et al. [36] proposed a UAV path-planning method based on improved Q learning in unknown environments. [PERSON] et al. [37] combined the double-screening sampling method with the deep deterministic policy gradient (DDPG) algorithm to propose the REL-DDPG algorithm. [PERSON] et al. [38] proposed a UAV formation positioning algorithm based on lidar to solve the problem of UAV formation control in GPS-blocked environments. [PERSON] et al. [39] proposed a constrained multi-agent reinforcement learning method for coordinating multiple UAVs to locate and track radio frequency (RF) mobile targets jointly. [PERSON] et al. [40] designed a long short-term memory (LSTM) DRL network to decompose the UAV navigation task into two simpler subtasks to reduce the amount of interactive data. [PERSON] et al. [41] proposed a path-planning algorithm based on multi-agent reinforcement learning to achieve efficient coverage of the target area. [PERSON] et al. [42] proposed a path-generation method based on deep reinforcement learning, which improved coverage efficiency and adaptability. [PERSON] et al. [43] Based on the reinforcement learning algorithm, the UAV can intelligently generate path-planning solutions according to the current environmental state, with more robust adaptability and flexibility. It can cope with complex and changing task requirements. [PERSON] et al. [44] proposed 3D UAV path-planning algorithms, categorizing them into Sampling-based, Node-based optimal, Mathematical model-based, Bio-inspired, and Multi-fusion approaches. The study critically evaluates each category's time complexity and applicability, with future work to enhance efficiency through algorithm integration. [PERSON], [PERSON], et al. [45] proposed a UAV communication network (UAVCN) path-planning framework, discussing network structure, performance evaluation, and generic and UAVCN-specific algorithms. [PERSON] [46] presented a novel occlusion-aware surveillance algorithm for UAVs that addresses the challenge of incomplete coverage due to urban obstacles. The proposed approach, which combines a variant of the 3D art gallery problem with the Traveling Salesman Problem for Dubins vehicles, is validated through high-fidelity simulations and demonstrates significant improvements in flight path planning in complex environments. [PERSON], [PERSON] [47] proposed to examine UAV-based weather monitoring, integrating cyber activities such as data collection, analysis, and planning with physical activities, including UAV deployment and weather event tracking. The intelligent optimization algorithm can find the optimal global solution through global search, but its real-time performance could be better. The neural network algorithm can handle complex nonlinear relationships and continuously optimize the UAV's trajectory planning through a large amount of data training to gradually improve the coverage efficiency; however, the algorithm strongly depends on data. The reinforcement learning algorithm can continuously learn through trial and error and optimize autonomously. It can be adjusted according to task requirements to optimize the UAV's trajectory planning. This paper proposes a multi-UAV area coverage algorithm based on the reinforcement learning algorithm.