A fixed-wing UAV swarm control method based on deep reinforcement learning

A technology of reinforcement learning and control methods, applied in vehicle position/route/height control, control/regulation systems, non-electric variable control, etc., can solve the problems of complex control of fixed-wing UAV clusters and few research results, and achieve The effect of improving autonomous decision-making ability, reducing workload, and high robust performance

Active Publication Date: 2022-08-09
NAT UNIV OF DEFENSE TECH
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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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  • A fixed-wing UAV swarm control method based on deep reinforcement learning
  • A fixed-wing UAV swarm control method based on deep reinforcement learning
  • A fixed-wing UAV swarm control method based on deep reinforcement learning

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Embodiment Construction

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] like figure 1 and Figure 5 As shown, a deep reinforcement learning-based fixed-wing unmanned aerial vehicle swarm control method of the present invention includes the following steps:

[0039] Step S1, offline training phase: establish a random UAV dynamics model, and perform action selection based on the evaluation of the Q function of the competitive double Q network (D3QN, Dueling Double Deep Q-Network);

[0040] Step S2, online execution stage: build a competitive dual-Q network, and load the trained network model, the network model and the action selection strategy are run on the onboard computer of the wingman, and the roll motion of the lead plane is given by the operator, The autopilots of the lead plane and the wingman are based on their respective rolling actions until the flight mission is completed.

[0041] In a ...

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Abstract

The invention discloses a fixed-wing unmanned aerial vehicle swarm control method based on deep reinforcement learning. Perform action selection; the competitive dual-Q network is a D3QN network; step S2, online execution phase: construct a competitive dual-Q network, and load the trained network model, the network model and the action selection strategy are run on the wingman's onboard On the computer, the lead plane's rolling action is given by the controller, and the autopilots of the lead plane and the wingman are based on their respective rolling actions until the flight mission is completed. The invention has the advantages of strong real-time performance and adaptability, and can transfer the strategy obtained by training in the simulation to the 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 swarm control method based on deep 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 insufficient performance of a single UAV, the above tasks usually require the cooperation of multiple UAVs to be completed efficiently. However, maneuvering multiple fixed-wing UAVs requires a lot of manpower to monitor the status of each aircraft, and coordinating multiple UAVs to carry out missions still faces certain challenges. [0003] "Consisten...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/10
CPCG05D1/101Y02T10/40
Inventor 闫超相晓嘉王菖牛轶峰尹栋吴立珍陈紫叶
Owner NAT UNIV OF DEFENSE TECH
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