边缘云资源感知下的无人机飞行规划

UAV Flight Planning under Edge Cloud Resource Awareness

在近距离为无人机(Unmanned Aerial Vehicles,UAVs)提供适当的地面控制和互补的计算与存储服务基础上,将使无人机能够支持广泛的应用。为实现此目标,部署在基站或其附近的边缘云平台至关重要。我们对现阶段提出的对于边缘云平台的资源约束的两种方法,即基于整数线性规划的多访问边缘计算(Multi-Access Edge Computing,MEC)感知无人机路径规划(MEC-Aware UAV Path Planning,MAUP)和加速MAUP(Accelerated MAUP,AMAUP)方法提出改进,并对其相关模拟环境提出限制条件,使得更加接近于真实环境。实验利用计算机仿真对原方案和改进方案进行评估,结果证明提出接近真实环境的必要性以及改进方案的有效性。

Based on providing appropriate ground control and complementary computing and storage services for Unmanned Aerial Vehicles(UAVs)at close range,UAVs will be able to support a wide range of applications.To achieve this goal,edge cloud platforms deployed at or near base stations are crucial.We have proposed two methods for resource constraints on edge cloud platforms at this stage,namely Multi-Access Edge Computing(MEC-Aware UAV Path planning based on integer linear programming,MAUP)and accelerated MAUP(Accelerated MAUP,AMAUP)methods propose improvements and put forward restrictions on their related simulation environments,making them closer to the real environment.The experiment uses computer simulation to evaluate the original plan and the improved plan.The results prove the necessity of proposing a solution that is close to the real environment and the effectiveness of the improved plan.