## 2 Literature Review  
The coordination of unmanned aerial vehicles (UAVs) for task allocation and path planning has garnered significant attention due to its critical role in autonomous systems. Early studies predominantly focused on static environments, where tasks and environmental conditions are fully known in advance. For instance, ref. [4] introduced a stochastic vehicle routing model with fixed task locations and deterministic travel times, foundational for UAV routing problems. Similarly, ref. [1] explored near-optimal coverage trajectories for UAVs over irregular fields, emphasizing static path optimization. Ref. [10] provided a comprehensive survey of coverage path planning techniques, applicable to static UAV missions, while ref. [11] extended this work with advancements in robotic path planning, including UAV contexts. These static approaches, though influential, fail to address real-world scenarios where tasks and conditions change unpredictably. To overcome these limitations, research has shifted toward dynamic task allocation and path planning. Ref. [3] investigated cooperative decision making for multi-UAV systems, proposing heuristic methods for dynamic task assignments. Ref. [12] developed decentralized cooperation strategies for UAVs in exploration tasks, focusing on real-time adaptability. Ref. [13] addressed dynamic task allocation in uncertain environments, introducing adaptive algorithms. Ref. [14] tackled cooperative task allocation under communication constraints, ensuring coordination with limited connectivity. Ref. [15] proposed a decentralized algorithm for dynamic task allocation in UAV swarms, integrating real-time communication. The need for continuous adaptation has popularized rolling horizon planning. Ref. [6] applied this approach to dynamic pickup-and-delivery problems, demonstrating its effectiveness in responding to new tasks. For UAVs, ref. [16] combined rolling horizon planning with reinforcement learning for adaptive mission management, though energy constraints were underexplored. Ref. [17] proposed a multi-objective genetic algorithm for dynamic UAV task allocation, yet it lacked full integration of energy and path planning. Ref. [18] derived the downlink coverage as a function of the UAV's altitude and antenna gain, then applied circle packing theory to design an efficient deployment strategy that maximizes coverage while preventing overlap among UAV coverage areas. Energy management is critical in UAV mission planning due to limited battery capacities. Ref. [5] analyzed energy consumption in UAV wireless communications, proposing energy-efficient trajectories for single-UAV scenarios. Ref. [19] modeled energy use in drone delivery systems, considering payload and distance. Ref. [20] optimized energy-aware trajectories, accounting for flight dynamics. Ref. [21] introduced energy-efficient path planning with adaptive speed control. Ref. [22] developed cooperative energy-aware coverage planning for UAVs. In multi-UAV contexts, ref. [23] used a genetic algorithm to optimize routes and charging schedules, though it assumed static tasks. Ref. [24] proposed a multi-objective framework balancing energy and mission time, but it overlooked dynamic task arrivals. Ref. [25] proposed to use NSGAII for simultaneously balancing the simultaneous pickup and delivery of medical supplies. Ref. [26] presented a mixed-integer linear programming model along with a heuristic algorithm to optimize the movement and recharging of UAVs deployed as flying base stations in 5G networks. Ref. [27] developed a mixed-integer linear programming model to plan UAV operations--including flight paths, energy management, and task execution--for detecting illegally parked electric scooters in urban areas. Optimization techniques underpin solutions to these challenges. Particle Swarm Optimization (PSO), introduced by [7], is widely used for its efficiency in large-scale problems. Ref. [28] analyzed PSO's stability and convergence, enhancing parameter tuning. Ref.