Multi-UAV path planning method based on edge computing dynamic task arrival

Abstract

The invention discloses a multi-UAV path planning method based on edge computing dynamic task arrival, including: establishing a system model for multi-UAV cooperative service users; constructing a multi-UAV path planning problem; simplifying the problem into a single time The optimization problem in the gap; the optimization problem is decomposed into the user frequency optimization sub-problem and the joint optimization problem of multi-UAV path optimization and user association sub-problems. Compared with a single UAV scenario, the present invention adds multiple tasks and energy queues of multiple UAVs, and adds scheduling constraints to ensure cooperative communication among multiple UAVs. In addition, in order to solve the complex multi-UAV path planning and scheduling problem, the Lyapunov queue optimization theory and block iterative descent algorithm are combined, and linear relaxation and continuous convex approximation are used to further reduce the complexity of the problem. The simulation results show that compared with the single-UAV system, the multi-UAV service ground user system has reduced UAV energy consumption and reduced queue backlog tasks.

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, UAVs combined with small base stations to provide real-time communication services for ground users have been widely used. Considering the limitation of the UAV's own energy consumption, in order to maximize the energy efficiency of the UAV, it is necessary to reasonably plan the flight path of the UAV. [0003] By optimizing its flight path, the drone can fly in a direction that is biased towards the user, thereby shortening the distance between the two and increasing the channel capacity. However, being too close to a certain user means that the distance from other users becomes farther (assuming users are evenly distributed in a certain range), resulting in po...

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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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