Fixed-wing UAV(unmanned aerial vehicle) cluster control method based on reinforcement learning

Abstract

The invention discloses a fixed-wing UAV(unmanned aerial vehicle) cluster control method based on reinforcement learning. The method comprises S1) training phase: establishing a stochastic UAV dynamicmodel, an actuator deep neural network and an evaluator deep neural network, and continuously collecting historical experience of interaction between an agent and environment and storing the historical experience in an experience pool; carrying out batch sampling randomly in the experience pool, continuously updating network parameters of an actuator and an evaluator and eventually forming and saving an evaluator network model; and S2)execution phase: for each wing plane, obtaining position and attitude information thereof through a sensor, loading the evaluator network model, outputting thebest rolling action of the wing plane by the evaluator based on a current system combined state, a lead plane roll angle set value being given by an operator, and repeating the steps above until finishing the flight mission. The method has the advantages of high real-time performance and adaptability and being capable of transferring a strategy obtained by training in simulation to real environment and the like.

Description

technical field [0001] The invention mainly relates to the technical field of unmanned aerial vehicles, in particular to a fixed-wing unmanned aerial vehicle cluster control method based on reinforcement learning. Background technique [0002] In recent years, with the continuous development of sensor technology, communication technology and intelligent control technology, UAV technology has made great progress. Fixed-wing UAVs have the characteristics of fast flight speed, strong endurance and large payload, and have been widely used in disaster search and rescue, border patrol, anti-terrorism and other fields. Due to the lack of performance of a single UAV, the above tasks usually require the cooperation of multiple UAVs to complete efficiently. However, operating multiple fixed-wing UAVs requires a lot of manpower to monitor the status of each aircraft, and coordinating multiple UAVs to perform tasks still faces certain challenges. [0003] Consensus theory is widely us...

AI Technical Summary

Problems solved by technology

Compared with rotorcraft, due to the non-holonomic constraints of the flight dynamics of fixed-wing UAVs, the swarm control of fixed-wing UAVs is more complicated, and the research results of applying reinforcement learning algorithms to the co-swarm control of fixed-wing UAVs are still less

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