Unmanned aerial vehicle flight trajectory optimization method based on machine learning in edge computing network

Abstract

The invention discloses an unmanned aerial vehicle flight trajectory optimization method based on machine learning in an edge computing network. The method comprises the following steps: in a cycle, an unmanned aerial vehicle departs from a position where a server is deployed, sequentially passes through all ground terminals and completes a communication task, and then returns to the initial position; when the unmanned aerial vehicle takes off, a flight scheduling table is calculated according to the position distribution of the ground terminals, and then the unmanned aerial vehicle flies through all the ground terminals in sequence according to the flight scheduling table; and in the flying process of the unmanned aerial vehicle, a local optimal trajectory of the unmanned aerial vehicle is calculated by utilizing a random method and a machine learning method in an iteration mode, the local optimal trajectory is added into a global optimal trajectory q, and the unmanned aerial vehicleflies along the global optimal trajectory q. According to the method, the flight distance and the flight time of the unmanned aerial vehicle can be effectively reduced and shortened, the flight cyclecan be shortened, and the transmission efficiency can be improved.

Description

technical field [0001] The invention relates to a method for optimizing the flight trajectory of an unmanned aerial vehicle, in particular to a method for optimizing the flight trajectory of an unmanned aerial vehicle based on machine learning in an edge computing network. Background technique [0002] Traditionally, wireless communication mainly employs fixed terrestrial infrastructure, such as terrestrial base stations (BS), access points, and relays. To effectively meet the growing and highly diverse transportation needs, various aerial communication platforms, such as balloons (Helikites) and unmanned aerial vehicles (UAVs), are utilized to provide wireless connectivity from the air. Unmanned Aerial Vehicles (UAVs) have received extensive attention in many fields in recent years: they can be applied in many different scenarios, including surveillance, surveillance, mobile relay, and data collection. In general, UAVs can provide line-of-sight (LoS) links, which provide g...

AI Technical Summary

Problems solved by technology

However, UAV communication systems still face many key challenges

One of them is the limited battery capacity, which makes the drone have to land to recharge, which severely limits the durability of the drone

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