Large-scale unmanned aerial vehicle cluster flight method based on deep reinforcement learning

Abstract

The invention discloses a large-scale unmanned aerial vehicle cluster flight method based on deep reinforcement learning, and the method comprises the steps: dividing a learning process of an unmanned aerial vehicle cluster anti-collision strategy into a plurality of courses in sequence, and enabling the unmanned aerial vehicle cluster scale of the next course to be larger than the unmanned aerial vehicle cluster scale of the previous course; constructing a curriculum reinforcement learning framework based on an actuator network and an evaluator network, and setting a group constant network based on an attention mechanism in the curriculum reinforcement learning framework; sequentially carrying out strategy learning on each course according to the course reinforcement learning framework to obtain a flight strategy of each unmanned aerial vehicle; and according to the empirical data of each unmanned aerial vehicle in the previous course of the current course, the executor network parameters and the evaluator network parameters of the current course in the strategy learning process are updated. According to the invention, the learning and training efficiency of the large-scale unmanned aerial vehicle can be effectively improved, the collision of the large-scale unmanned aerial vehicle cluster during flight is effectively avoided, and the generalization ability is strong.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicles, in particular to a large-scale unmanned aerial vehicle cluster flight method based on deep reinforcement learning. Background technique [0002] In recent years, with the continuous development of robot technology, machine learning technology and intelligent control technology, UAV autonomous control 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 regional reconnaissance, disaster search and rescue, border control and other tasks. The above tasks usually require the coordination of multiple UAVs to improve the efficiency of task completion. However, the difficulty of obtaining the autonomous flight and obstacle avoidance behavior of UAV swarms is closely related to the number of UAVs and the complexity of tasks, and is still a very challenging theoretical problem. ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems existing in the prior art, the present invention provides a large-scale unmanned aerial vehicle cluster flight method based on deep reinforcement learning, which can solve the dynamic change o

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