Unmanned aerial vehicle cluster meeting method based on deep reinforcement learning

A technology of reinforcement learning and unmanned aerial vehicles, applied to mechanical equipment, combustion engines, internal combustion piston engines, etc., to achieve the effect of ensuring control stability

Active Publication Date: 2020-06-05
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

Judging from the results of collaborative missions of UAV clusters realized in China, my country's UAV clusters have more research on how to effectively prevent collisions when UAV clusters cooperate to perform tasks

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  • Unmanned aerial vehicle cluster meeting method based on deep reinforcement learning
  • Unmanned aerial vehicle cluster meeting method based on deep reinforcement learning
  • Unmanned aerial vehicle cluster meeting method based on deep reinforcement learning

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Embodiment Construction

[0049] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0050] A UAV swarm rendezvous method based on deep reinforcement learning, which is divided into a training phase and an execution phase, including the following steps:

[0051] Step 1: Training phase, the specific steps are as follows:

[0052] Step 1-1: Set a fixed area in the rendezvous task scene as the rendezvous area of ​​the UAV cluster, and obtain the location information of the center point of the area (x 0 ,y 0 ), and randomly initialize M UAVs in the mission scene to obtain UAV status information, including the initial position (x i ,y i ), i=1...M and initial velocity (v xi ,v xi ), i=1...M. In this case, the scene is a continuous environment data unit of 200*200, and a fixed-sized circular rendezvous area is set in the center of the task scene, and 20 drones are randomly distributed in the scene, and the initial value of each drone is...

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Abstract

The invention provides an unmanned aerial vehicle cluster meeting method based on deep reinforcement learning. In the training stage, a fixed area is set in the meeting task scene to serve as a meeting area of the unmanned aerial vehicle cluster, area center point position information is obtained, a deep neural network for judging movement of the unmanned aerial vehicle cluster is established, thedeep neural network is trained, and after training is completed, a final deep neural network is obtained; and in the execution stage: the input data is inputted into the trained deep neural network for judgment. According to the method, the state space and the behavior space of the unmanned aerial vehicle cluster task are expanded, the practicability is high for incomplete scene information, thetask-oriented unmanned aerial vehicle cluster unified decision network is constructed, and unified command and control of the decision network for the uncertain number of unmanned aerial vehicles arerealized.

Description

technical field [0001] The invention relates to the fields of machine learning and path planning, in particular to a method for rendezvous of drone swarms. Background technique [0002] In order to achieve precise strikes by UAV clusters on a certain target area or to complete the reconnaissance and search tasks in a certain area, and improve the success rate of completing tasks, it is often necessary for multiple UAVs to fly to a certain target area from different directions to complete the rendezvous task . Traditionally, basic consensus algorithms and consensus algorithms based on past state differences are used to solve UAV cluster rendezvous tasks. These algorithm systems have slow convergence speed, long task completion time, and low effectiveness and feasibility. [0003] The current research on the behavior control direction of UAVs using artificial intelligence methods mainly stays in the research and application of individual UAVs, and there are few researches on ...

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Application Information

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IPC IPC(8): G05D1/10
CPCG05D1/104Y02T10/40
Inventor 张耀中许佳林姚康佳
Owner NORTHWESTERN POLYTECHNICAL UNIV
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