Unmanned aerial vehicle autonomous flight control method and system based on deep learning

Abstract

The invention discloses an unmanned aerial vehicle autonomous flight control method and system based on deep learning. According to the invention, a computer vision target detection and orientation estimation algorithm of an artificial neural network based on deep learning is put forward, images are collected by using a multi-rotor unmanned aerial vehicle carrying a cradle head camera, target detection and target orientation estimation are performed by using an operation unit for operating the neural network, and finally a control instruction is fed back to an unmanned aerial vehicle flight controller by combining a control algorithm. A distributed data collection, operation and control loop comprising a data collection unit, a deep learning operation unit and a flight control unit is constructed. Experimental results show that the unmanned aerial vehicle autonomous flight control method and system can achieve the effects that real-time neural network operations are performed, and thusthe unmanned aerial vehicle discovers a target quickly and autonomously, gets close to the target and tracks the target with a specific attitude by combining the control algorithm.

Description

technical field [0001] The invention belongs to the field of computer vision and automatic control, and in particular relates to a method and system for autonomous flight control of unmanned aerial vehicle based on deep learning. Background technique [0002] Compared with manned aircraft, UAVs have the advantages of low cost, small size, convenient use, low production and maintenance costs, strong mobility and strong survivability. Since there is no human pilot, the UAV is not limited by the physiological and life risks of personnel, and is suitable for "boring and dangerous" tasks such as intelligence collection, geological survey, low-altitude reconnaissance and anti-terrorist strikes. With the development of science and technology, the production cost of drones has been further reduced, and it has begun to develop in civilian and scientific research fields, such as gas pipeline monitoring, area coverage monitoring, disaster emergency search and rescue, agricultural plant...

AI Technical Summary

Problems solved by technology

[0005] The traditional UAV flight path control is based on the waypoint setting, relying on the internal program of the flight controller to make the UAV fly along the waypoints in sequence, with low autonomy, poor flexibility, and relatively slow speed

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