Abstract
To solve the problem that distributed fixed wing formation cannot know all the other aircraft states through the ground station and may collide, the improved artificial potential field method based on binocular stereo vision was proposed. This method makes the fixed wing aircraft not need to obtain the position information of other fixed wing aircraft from the ground station, but only need the binocular stereo vision module to obtain the relative position and relative speed information of other fixed wing aircraft. These two parameters are used to improve the traditional artificial potential field method to meet the requirements of distributed fixed wing formation flight. The experimental results show that the improved artificial potential field method improves the safety and reliability of distributed fixed wing formation flying.
Highlights
With the development of artificial intelligence technology, intelligent UAV cluster cooperation to complete complex tasks has become a research hotspot
In the distributed control method, a single UAV only needs to sense the status of other UAVs around it, and make self-adjustment according to other UAVs, without sharing information, making it more flexible [2]
Based on the above analysis, this paper proposes an improved artificial potential field method which combines stereo vision with deep learning to realize the UAV's perception of the environment and obstacle avoidance
Summary
With the development of artificial intelligence technology, intelligent UAV cluster cooperation to complete complex tasks has become a research hotspot. The main obstacle avoidance measurement methods are: ultrasonic measurement, 3D lidar survey, monocular vision detection and binocular vision detection. The feasibility of these methods is discussed respectively in the application scenario of obstacle avoidance of distributed fixed wing formation. In the selection of obstacle avoidance algorithm, artificial potential field method does not need global information and is suitable for distributed cluster control. Based on the above analysis, this paper proposes an improved artificial potential field method which combines stereo vision with deep learning to realize the UAV's perception of the environment and obstacle avoidance. The second section introduces how to use binocular vision module to obtain the obstacle information, the third section introduces how to use the obstacle information to improve the artificial potential field method, and the fourth part has carried on the simulation verification test [7,8]
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