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Fixed-wing unmanned aerial vehicle autonomous control cooperation strategy training method

A technology of autonomous control and training method, applied in non-electric variable control, control/regulation system, three-dimensional position/channel control, etc. The effect of trial and error cost and efficient trial and error cost

Active Publication Date: 2020-12-04
NANJING UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, simple reinforcement learning also has its limitations. The space for exploration and learning is too large, and the effect depends heavily on parameter tuning tricks, making training difficult.

Method used

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  • Fixed-wing unmanned aerial vehicle autonomous control cooperation strategy training method
  • Fixed-wing unmanned aerial vehicle autonomous control cooperation strategy training method
  • Fixed-wing unmanned aerial vehicle autonomous control cooperation strategy training method

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Embodiment Construction

[0023] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0024] A fixed-wing unmanned aerial vehicle autonomous control cooperative strategy training method, comprising the following steps:

[0025] Step 1: Build a simulator Em_s for fixed-wing UAV control based on dynamics, and the visualization part of simulator Em_s is implemented based on the unity3D engine. The UAV simulates the environment E here s The training process in is defined as the tuple form of the Markov decision process (MDP), where S is the state information of the UAV, A is the acti...

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Abstract

The invention discloses a fixed-wing unmanned aerial vehicle autonomous control cooperation strategy training method, which comprises the following steps of (1) constructing a fixed-wing unmanned aerial vehicle control simulation environment Es based on dynamics, collecting real trajectory data of a pilot controlling an unmanned aerial vehicle, and learning in a supervised learning mode to obtainan unmanned aerial vehicle flight control strategy, (2) constructing a simplified abstract environment Ea from which flight control is stripped, creating two groups of unmanned aerial vehicle groups for grouping confrontation, and learning by using an APEX_QMIX algorithm to obtain a cooperation strategy, (3) combining the flight control strategy and the cooperation strategy in a layered reinforcement learning mode, and learning in a simulation environment Es to obtain a fusion strategy, and (3) migrating to a real environment. The method is of great significance in a real scene and has the advantages of being good in generalization, low in cost, high in robustness and the like.

Description

technical field [0001] The invention relates to a fixed-wing unmanned aerial vehicle autonomous control cooperation strategy training method based on layered reinforcement learning and multi-agent reinforcement learning, and the technical field of unmanned aerial vehicle autonomous control cooperation strategy. Background technique [0002] For the traditional autonomous control and cooperation strategy of fixed-wing UAV, it mainly adopts the method of automatic control, artificial modeling, and formulation of strategy. Rely on the development of flight rules by experts in the relevant field. The cost is high and due to the frequent scene changes in the complex and changing environment, there are a large number of situations that are not considered in the flight rules. Therefore, flight rules are usually unable to deal with complex and changing environments, and their capabilities are low. [0003] Recently, with the vigorous development of machine learning technology, rei...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/10
CPCG05D1/104
Inventor 俞扬詹德川周志华王超袁雷陈立坤黄宇洋庞竟成
Owner NANJING UNIV
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