Multi-unmanned aerial vehicle path planning method based on edge computing dynamic task arrival

Abstract

The invention discloses a multi-unmanned-aerial-vehicle path planning method based on edge computing dynamic task arrival. The method comprises the steps that a system model of multi-unmanned-aerial-vehicle cooperative service users is established; constructing a multi-unmanned aerial vehicle path planning problem; simplifying the problem into an optimization problem in a single time slot; and decomposing the optimization problem into a user frequency optimization sub-problem and a joint optimization problem of multi-unmanned aerial vehicle path optimization and user association sub-problems.Compared with a single unmanned aerial vehicle scene, multiple tasks and energy queues of multiple unmanned aerial vehicles are added, and scheduling limitation conditions are added to guarantee cooperative communication among the multiple unmanned aerial vehicles. In addition, in order to solve the complex multi-unmanned aerial vehicle path planning and scheduling problem, the Lyapunov queue optimization theory and the block iterative descent algorithm are combined, and the problem complexity is further reduced through linear relaxation and continuous convex approximation. A simulation resultshows that compared with a single-unmanned-aerial-vehicle system, the multi-unmanned-aerial-vehicle service ground user system has the advantages that the energy consumption of the unmanned aerial vehicles is reduced, and the queue backlog task load is reduced.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, in particular to a multi-UAV path planning method based on edge computing dynamic task arrival. Background technique [0002] With the development of the times and the needs of users for communication and data processing, drones combined with small base stations to provide real-time communication services for ground users have been widely used. Considering the energy consumption limitation of the UAV itself, in order to maximize the energy efficiency of the UAV, it is necessary to plan the flight path of the UAV reasonably. [0003] By optimizing its flight path, the drone can fly in a direction biased toward the user, reducing the distance between the two and increasing channel capacity. However, being too close to a certain user means that the distance from other users becomes longer (assuming that users are evenly distributed within a certain range), resulting in poor communica...

AI Technical Summary

Problems solved by technology

[0004] To sum up, the path planning scheme for drones at this stage does not consider the energy consumption of drones, and ignores the waiting delay caused by the limited processing capacity of the base station server itself in the process of serving users with drones

At the same time, due to the limited ability of single UAV path planning, there is a situation of low communication efficiency in a large area

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