Route optimizing method and device of communication unmanned aerial vehicle

Abstract

The invention discloses a route optimizing method and device of a communication unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle communication. The method includes the steps of obtaining a user group and geographic positions thereof, dividing the user group into K multicast groups, determining K initial hovering positions corresponding to the K multicast groups one to one, determining the shortest initial flight route of the K initial hovering positions, and optimizing the initial hovering positions of the unmanned aerial vehicle one by one to obtain the Kfinal hovering positions corresponding to the K multicast groups and the final flight route. In the method, by dividing the user group into a plurality of multicast groups, finding a proper hoveringposition in each multicast group so that the unmanned aerial vehicle can transmit files at a high transmission rate at the position and designing the flight trajectory of the unmanned aerial vehicle to find the shortest route passing through all the hovering positions and returning to the initial position, multicast service transmission tasks are completed by the unmanned aerial vehicle with the minimum total energy consumption.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a method and equipment for optimizing the path of a communication unmanned aerial vehicle. Background technique [0002] On the one hand, with the widespread application of unmanned aerial vehicles (referred to as UAVs) in the industry in recent years, UAV communication has also become a research hotspot in the field of wireless communication. Compared with the traditional ground communication system, the UAV communication system has the advantages of low cost, fast deployment, high flexibility, excellent channel conditions, and controllable movement. Therefore, UAV communication will gradually become an indispensable part of the future wireless communication field. missing part. However, UAV wireless communication is not perfect and faces many challenges, among which insufficient energy is one of the biggest factors restricting the development of U...

AI Technical Summary

Problems solved by technology

[0007] First of all, the existing technology does not take into account the energy consumption of UAVs when performing tasks; secondly, the user cannot be grouped, or the grouping method is not flexible enough, so that the multicast transmission rate cannot be greatly improved, thus increasing the number of users. The time and energy consumed by humans and machines to perform tasks; finally, the existing technology does not use data-driven methods to solve this multicast transmission problem. Therefore, it cannot effectively reflect the distribution of users, which is not conducive to building an accurate model. In the past, the number of multicast groups was determined based on experience, or all possible multicast groups were traversed. Experience is often inaccurate when the number of users is large. Inefficient and computationally expensive

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