The invention relates to a multi-unmanned aerial vehicle cooperation edge calculation method based on reinforcement learning. Wherein the multiple unmanned aerial vehicles serve as mobile base stations and provide calculation services for ground users; presetting the period frequency and the storage size of a central processing unit for providing calculation service on each unmanned aerial vehicle; taking task calculation service delay, energy consumption and unmanned aerial vehicle track weighted sum minimization as optimization targets; carrying out pre-training by adopting a genetic algorithm, and taking a fitness function of the genetic algorithm as a reward function of a multi-agent depth deterministic strategy gradient algorithm; determining an unloading strategy and a track of each unmanned aerial vehicle by adopting the algorithm; and a multi-intelligent depth deterministic strategy gradient algorithm is adopted to determine a delivery unmanned aerial vehicle of each task calculation result, and the result is fed back to a user. According to the method, the flight path of each unmanned aerial vehicle, the user service quality and the energy efficiency of the whole system are fully considered, timely delivery of tasks is ensured, and the whole energy consumption of the system is reduced.
本发明涉及一种基于强化学习的多无人机协作边缘计算方法。其中多架无人机作为移动基站,为地面用户提供计算服务;预设每个无人机上提供计算服务的中央处理器的周期频率、存储大小;以任务计算服务延迟、能量消耗、无人机轨迹加权和最小为优化目标;采用遗传算法进行预训练,并将遗传算法的适应度函数作为多智能体深度确定性策略梯度算法的奖励函数;采用该算法确定各个无人机的卸载策略及轨迹;采取多智能深度确定性策略梯度算法确定各个任务计算结果的交付无人机并将结果反馈给用户。本方法充分考虑各个无人机的飞行轨迹,用户服务质量和整体系统的能量效率,保证任务的及时交付并降低系统的整体能耗。
Multi-unmanned aerial vehicle cooperation edge calculation method based on reinforcement learning
一种基于强化学习的多无人机协作边缘计算方法
2025-06-20
Patent
Electronic Resource
Chinese
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