A cooperative strategy training method for autonomous control of fixed-wing unmanned aerial vehicles

Abstract

The invention discloses a fixed-wing unmanned aerial vehicle autonomous control cooperation strategy training method, comprising the following steps: (1) constructing a fixed-wing unmanned aerial vehicle control simulation environment E based on dynamics s , collect the real trajectory data of the pilot controlling the UAV, and learn the flight control strategy of the UAV through supervised learning; (2) Construct a simplified abstract environment E that strips the flight control a , create two groups of unmanned aerial vehicle swarms for group confrontation, and use the APEX_QMIX algorithm to learn the cooperative strategy; (3) Combine the flight control strategy and the cooperative strategy in the way of hierarchical reinforcement learning, and in the simulation environment E s Integrate the strategies learned in middle school; (3) Migrate to the real environment. The method of the invention is of great significance in real scenes, and has the characteristics of good generalization, low cost, strong 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...

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.

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