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Unmanned aerial vehicle perception and obstacle avoidance integrated method and equipment based on deep reinforcement learning

A UAV, deep technology, applied in vehicle position/route/altitude control, non-electric variable control, instruments, etc., can solve problems such as low accuracy and achieve the effect of improving autonomy

Inactive Publication Date: 2020-08-04
BEIHANG UNIV
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  • Unmanned aerial vehicle perception and obstacle avoidance integrated method and equipment based on deep reinforcement learning
  • Unmanned aerial vehicle perception and obstacle avoidance integrated method and equipment based on deep reinforcement learning
  • Unmanned aerial vehicle perception and obstacle avoidance integrated method and equipment based on deep reinforcement learning

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[0022] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by those skilled in the art to which the present invention belongs.

[0024] The traditional autonomous obstacle avoidance algorithm is generally divided into four steps: obtain the required observations in the environment, perform state estimation, modeling and pr...

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Abstract

The embodiment of the invention provides an unmanned aerial vehicle perception and obstacle avoidance control integrated method based on a deep deterministic strategy gradient. The method comprises the steps of designing a deep deterministic strategy gradient neural network; designing rewards required by training; building an obstacle avoidance scene of an unmanned aerial vehicle in a simulation environment; and training the network in combination with the simulation environment.

Description

technical field [0001] The present invention relates to the field of autonomous control of unmanned aerial vehicles, and more specifically, relates to an integrated method and device for unmanned aerial vehicle perception and obstacle avoidance control based on depth deterministic strategy gradient. Background technique [0002] At present, for the problem of UAV autonomous obstacle avoidance control, the traditional track planning method is mainly used for obstacle avoidance. For example, Rapidly-exploring Random Tree (RRT for short) and artificial potential field algorithm, etc., but the traditional method has its disadvantages. The RRT algorithm has slow convergence speed and tortuous track. Issues such as small values ​​and oscillations. In addition, discontinuous direction commands may be directly output through image information. For example, some methods are based on the hierarchical structure of Deep Q-Network (DQN for short), and these hierarchical Q-networks are ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/10
CPCG05D1/101
Inventor 蔡志浩王隆洪赵江王英勋
Owner BEIHANG UNIV
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