A Deep Reinforcement Learning Approach for Continuous Coverage of Specific Areas by Multi-UAV Systems

Abstract

The invention discloses a deep reinforcement learning method for continuous coverage of a specific area by a multi-unmanned aerial vehicle system. The steps include: step S1: establishing a deep convolutional neural network model for feature extraction of the area to be covered; step S2: based on reinforcement learning actor‑critic network, establish a deep reinforcement learning model for multi-UAV systems to continuously cover a specific area, respectively establish a multi-UAV system control decision model and a bidirectional cyclic neural network model for action value functions; An individual reward function is designed for the UAV decision-making system; Step S4: Based on the reinforcement learning policy gradient method, the neural network model in steps S1 and S2 is trained. The invention has the advantages of good robust performance, strong scalability, short coverage period, good coordination and the like.

Description

technical field [0001] The invention mainly relates to the technical field of coordinated control of unmanned aerial vehicles, in particular to a deep reinforcement learning method used for a multi-unmanned aerial vehicle system to continuously cover a specific area. Background technique [0002] UAV swarm area coverage is an important research problem, which mainly solves how to cover a designated area through large-scale UAVs, so as to achieve the best performance of the swarm system, such as the shortest coverage time and the highest coverage rate. The area coverage of UAV swarms has a very wide range of applications, such as geographic mapping, search and rescue, and disaster monitoring. [0003] The early research on coverage planning is mainly aimed at the method research of single-machine coverage of designated area, such as scanning method, area segmentation, and process planning. In recent years, researchers have focused on multi-UAV cooperative area coverage, such...

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Problems solved by technology

[0004] Among the traditional methods mentioned above, some methods only study coverage-related issues, and do not consider the continuous coverage of the area and the coordination between UAVs; although some methods study the problem of continuous coverage, they do not minimize the continuous coverage. coverage period, and these methods are poor in robustness, weak in scalability, and do not deal with the control problem of heterogeneous UAVs

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