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Unmanned aerial vehicle maneuvering target tracking method based on DDPG transfer learning

A technology of maneuvering target tracking and transfer learning, applied in the field of robot intelligent control, can solve problems such as the lack of consideration of the transfer ability of neural network time cost and difficult task training

Active Publication Date: 2020-09-15
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

However, this method does not consider the time cost of neural network fitting and its migration ability, making the task difficult to train

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  • Unmanned aerial vehicle maneuvering target tracking method based on DDPG transfer learning
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  • Unmanned aerial vehicle maneuvering target tracking method based on DDPG transfer learning

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Embodiment Construction

[0099] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0100]The UAV maneuvering target tracking method based on DDPG migration learning proposed by the present invention, the overall process is as follows figure 1 shown. Below in conjunction with accompanying drawing and specific embodiment, this technical solution is further clearly and completely described:

[0101] Step 1: Construct the Markov model (S, A, O, R, γ) for UAV maneuvering target tracking, where S is the input state of the UAV, A is the output action of the UAV, and O is The observation space of the UAV sensor, R is the reward function, and γ is the discount coefficient;

[0102] Step 1-1: Define the state space of the Markov model, that is, the input state S:

[0103] Combined with the UAV state, target state, and obstacle state information, set the model input state as:

[0104]

[0105] Where: UAV status S uav =[x uav ,y uav ,v uav ,θ u...

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Abstract

The invention relates to an unmanned aerial vehicle maneuvering target tracking method based on DDPG transfer learning, and the unmanned aerial vehicle maneuvering target tracking method carrying outtask decomposition, initializes an environment state, neural network parameters and other hyper-parameters, and carries out the training of a neural network. At the beginning of each round, the unmanned aerial vehicle executes an action to change the speed and the course angle, to obtain a new state, stores the experience of each round in an experience pool to serve as a learning sample, and continuously iterates and updates parameters of the neural network. And when the training is completed, the neural network parameters trained by the sub-tasks are stored, and are migrated to the unmanned aerial vehicle maneuvering target tracking network in the next task scene until the final task is completed.

Description

technical field [0001] The invention relates to a method for tracking a maneuvering target of an unmanned aerial vehicle based on DDPG transfer learning, which belongs to the field of robot intelligent control. Background technique [0002] With the continuous development of drone technology, drones have been widely used in the civil field. Among the many tasks of UAVs, surveillance and reconnaissance tasks are performed most. If UAVs can independently and accurately track other maneuvering targets, expand the scope of surveillance, and effectively avoid the threat area, it can be greatly improved. Improve surveillance, reconnaissance and even attack efficiency. [0003] Most of the existing research on UAV maneuvering targets focuses on the state estimation and measurement information processing of the maneuvering target. Few studies have been done on how to determine the maneuvering behavior of the UAV after the state of the maneuvering target is determined, so that it ca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/277
CPCG06T7/277G06T2207/20081G06T2207/20084G06T2207/20076
Inventor 李波杨志鹏高晓光万开方梁诗阳马浩
Owner NORTHWESTERN POLYTECHNICAL UNIV
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