DDQN-based autonomous guidance maneuver decision-making method for unmanned aerial vehicle

Abstract

The invention provides a DDQN-based autonomous guidance maneuver decision-making method for an unmanned aerial vehicle, which is an unmanned aerial vehicle autonomous guidance maneuver decision-makingmethod based on combination of a priority sampling double-depth Q learning algorithm and a Markov decision-making process; the double-Q learning algorithm is introduced to improve the iteration modeof the deep Q learning algorithm, and the training efficiency is improved. A priority sampling method is adopted to promote rapid convergence of the algorithm, and the diversity of historical data isbetter utilized; the unmanned aerial vehicle can realize autonomous guidance maneuvering decision making according to the external flight environment state, and completes autonomous guidance maneuvering decision making under a fixed target point to effectively improve the flight autonomy of the unmanned aerial vehicle. According to the method, the over-fitting problem existing in the DQN algorithmis solved, the offline training efficiency of the autonomous guidance maneuvering decision-making method of the unmanned aerial vehicle is greatly improved, the autonomy of the unmanned aerial vehicle in the flight process is enhanced, and the task execution efficiency of the unmanned aerial vehicle is improved.

Description

technical field [0001] The invention relates to the field of flight maneuver decision-making and artificial intelligence, in particular to a maneuver decision-making method. Background technique [0002] With the rapid development of electronic technology and drone technology in recent years, the performance of drones has improved rapidly, and various new functions have emerged one after another. Among them, how to improve the autonomous performance of UAV flight and avoid human error has gradually become the research direction that researchers from all over the world are focusing on. In the traditional method, when the UAV flies to a specific location, it needs to plan the flight route in advance, and then the UAV pilot controls the UAV to fly according to the predetermined route. Currently, some navigation control methods are used instead of realizing the UAV's manipulator. Usually, traditional methods are implemented based on game theory, influence diagram, dynamic Baye...

AI Technical Summary

Problems solved by technology

However, the deep Q-learning algorithm (Deep Q-Learning, DQN) itself has the problem of over-fitting the state-action value

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