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A swarm control method for fixed-wing unmanned aerial vehicles based on reinforcement learning

A technology for strengthening learning and control methods, applied in the field of unmanned aerial vehicles, can solve the problems of complex control of fixed-wing unmanned aerial vehicles and few research results, achieve strong real-time and adaptability, and reduce workload.

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 swarm control method for fixed-wing unmanned aerial vehicles based on reinforcement learning
  • A swarm control method for fixed-wing unmanned aerial vehicles based on reinforcement learning
  • A swarm control method for fixed-wing unmanned aerial vehicles based on reinforcement learning

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Embodiment Construction

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] like figure 1 and Figure 4 As shown, a reinforcement learning-based fixed-wing unmanned aerial vehicle swarm control method of the present invention includes:

[0034] Step S1, training phase: establish a random UAV dynamics model, an actuator deep neural network and an evaluator deep neural network, continuously collect the historical experience of the interaction between the agent and the environment, and store it in the experience pool; randomize from the experience pool Perform batch sampling, continuously update the network parameters of the executor and the evaluator, and finally form a network model for saving the evaluator;

[0035] Step S2, the execution stage: the wingman obtains its own position and attitude information through the sensor, and loads the evaluator network model. The evaluator outputs the optimal roll...

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Abstract

The invention discloses a fixed-wing unmanned aerial vehicle swarm control method based on reinforcement learning. The historical experience of the interaction between the agent and the environment is stored in the experience pool; batch sampling is randomly performed from the experience pool, and the network parameters of the executor and the evaluator are continuously updated, and finally the network model of the preservation evaluator is formed; step S2, execute Stage: The wingman obtains its own position and attitude information through the sensor, loads the network model of the evaluator, and the evaluator outputs the optimal roll action of the wingman according to the current joint state of the system, and the set value of the lead plane's roll angle is given by the operator; until the flight is completed Task. The invention has the advantages of strong real-time performance and adaptability, and can transfer the strategy trained in the simulation to the real environment.

Description

technical field [0001] The invention mainly relates to the technical field of unmanned aerial vehicles, in particular to a swarm control method of fixed-wing unmanned aerial vehicles 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 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] Consistency...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/10
CPCG05D1/104Y02T10/40
Inventor 王菖闫超相晓嘉牛轶峰尹栋吴立珍陈紫叶
Owner NAT UNIV OF DEFENSE TECH
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