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Autonomous Obstacle Avoidance System and Method for UAV Hydrological Monitoring Based on Binocular Vision

A hydrological monitoring and binocular vision technology, applied in control/regulation systems, water resource assessment, climate sustainability, etc., can solve the problem of damage to autonomous obstacle avoidance equipment, exposure to the outside of the drone, and increase the hydrological monitoring cycle and cost to achieve the effect of improving the accuracy of obstacle avoidance

Active Publication Date: 2022-07-12
GUIZHOU EDUCATION UNIV
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Problems solved by technology

However, for actual hydrological monitoring operations, UAVs will inevitably encounter obstacles during flight or landing. In order to effectively avoid these obstacles, the existing technology usually installs special autonomous obstacle avoidance equipment on UAVs. Control the flight of UAVs, but this kind of autonomous obstacle avoidance equipment is not only expensive, but also exposed on the outside of the UAV. Accuracy of obstacle avoidance during monitoring and increase cycle and cost of hydrological monitoring

Method used

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  • Autonomous Obstacle Avoidance System and Method for UAV Hydrological Monitoring Based on Binocular Vision
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  • Autonomous Obstacle Avoidance System and Method for UAV Hydrological Monitoring Based on Binocular Vision

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[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0074] see figure 1 , which is a schematic structural diagram of an autonomous obstacle avoidance system for UAV hydrological monitoring based on binocular vision provided by an embodiment of the present invention. The autonomous obstacle avoidance system for UAV hydrological monitoring based on binocular vision includes a UAV body, an ultrasonic ranging module, a binocular camera module, a data processing module and a flight control...

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Abstract

The invention provides an autonomous obstacle avoidance system and method for unmanned aerial vehicle hydrological monitoring based on binocular vision, which can quickly and accurately identify obstacles for unmanned aerial vehicles in the process of hydrological monitoring by means of a combination of ultrasonic ranging and binocular photography Therefore, the UAV can automatically avoid obstacles, thereby improving the accuracy of UAV obstacle avoidance in the process of hydrological monitoring, shortening the period of hydrological monitoring, and reducing monitoring costs.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle hydrological monitoring, in particular to an autonomous obstacle avoidance system and method for unmanned aerial vehicle hydrological monitoring based on binocular vision. Background technique [0002] At present, drones have been used to monitor the hydrology of large areas of water bodies such as rivers or lakes. Through the inspection and shooting of drones over the corresponding water body areas, it is possible to save a lot of manpower and material resources. Comprehensive and rapid monitoring of water body areas hydrological monitoring. However, for the actual hydrological monitoring operation, the UAV will inevitably encounter obstacles during flight or landing. In order to avoid these obstacles effectively, the existing technology usually installs special autonomous obstacle avoidance equipment on the UAV. Control the flight of the drone, but this kind of autonomous obstac...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/10
CPCG05D1/106Y02A90/30
Inventor 崔忠伟桑海伟吴恋杨国荣王晴晴游顺才左欣
Owner GUIZHOU EDUCATION UNIV
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