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Bridge detection unmanned aerial vehicle autonomous navigation and stability control method based on reinforcement learning

A stability control method and bridge detection technology, applied in three-dimensional position/channel control, attitude control, non-electric variable control and other directions, can solve the problems of lack of positioning signals under the bridge, unable to stabilize flight control, etc., and achieve fast and stable flight control. Effect

Pending Publication Date: 2021-12-21
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0018] The purpose of the present invention is to provide an autonomous navigation and stable control method for bridge detection UAVs based on reinforcement learning to solve the problems of lack of positioning signals under the bridge and unsteady flight control under strong wind interference for UAV bridge detection technology

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  • Bridge detection unmanned aerial vehicle autonomous navigation and stability control method based on reinforcement learning
  • Bridge detection unmanned aerial vehicle autonomous navigation and stability control method based on reinforcement learning
  • Bridge detection unmanned aerial vehicle autonomous navigation and stability control method based on reinforcement learning

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Embodiment Construction

[0052] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0053] The invention provides a bridge detection UAV autonomous navigation and stability control method based on reinforcement learning, which specifically includes the following steps:

[0054] Step 1: Carry out dynamic modeling of the bridge inspection UAV based on the Newton-Euler method;

[0055] Step 2: Autonomous obstacle avoidance of bridge detection UAV based on neural network reinforcement learning;

[0056] Step 3: Design the binocular vision and IMU combined autonomous navigation algorithm under BIM;

[0057] Step 4: Design a stable control scheme for the bridge detection UAV under the interference of strong wind field.

[0058] In some embodiments, in step 1, it is necessary to carry out a dynamic model to the detection bridge detection UAV:

[0059] Considering that the bridge detection UAV is a nonlinear, strongly coupled, un...

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Abstract

The invention discloses a bridge detection unmanned aerial vehicle autonomous navigation and stability control method based on reinforcement learning. The method comprises the following steps: 1, carrying out dynamic modeling on a four-rotor unmanned aerial vehicle for detecting a bridge based on a Newton-Euler method; 2, considering surrounding environment information and detection constraint conditions of a to-be-detected bridge, and proposing to realize autonomous obstacle avoidance flight of the unmanned aerial vehicle based on neural network reinforcement learning; 3, adopting binocular vision and IMU combined autonomous navigation under the assistance of a bridge building information model; and 4, carrying out attitude control by adopting an active disturbance rejection algorithm in inner loop control of the system, carrying out position control by adopting PID control in outer loop control, and achieving stable control of the bridge detection unmanned aerial vehicle under strong wind interference. The problems that in the unmanned aerial vehicle bridge detection technology, under-bridge positioning signals are lost, and flight control cannot be stabilized under strong wind interference are solved.

Description

technical field [0001] The invention belongs to the technical field of bridge detection, and in particular relates to an autonomous navigation and stability control method of a bridge detection UAV based on reinforcement learning. Background technique [0002] my country's transportation infrastructure construction is developing rapidly, and bridge construction is growing rapidly at an average annual rate of 20,000. By the end of 2018, my country has built more than 90,000 long-span bridges and more than 5,000 extra-long-span bridges, of which the main span exceeds 400. There are 105 super long-span bridges. The safety of existing bridges has become a core issue related to the national economy and national security. [0003] At present, most bridge inspection operations in my country still use three traditional technical methods: [0004] (1) Bridge inspection vehicle: it is the main method of bridge inspection at present. This type of method uses bridge inspection vehicles...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/08G05D1/10
CPCG05D1/0825G05D1/106
Inventor 黄攀峰方国涛张夷斋张帆常海涛
Owner NORTHWESTERN POLYTECHNICAL UNIV
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