Cooperative Path Optimization for Multi-UAVs Based on Dynamic Target Positioning and Tracking

Zhao, Yu; Zhang, Xiaoyi

doi:10.1109/icras.2019.8809068

Cited by 3 publications

(1 citation statement)

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“…However, the autonomous navigation capability of UAV has become the main factor restricting the development of UAV to long-distance and long endurance [2]. The sensors carried by UAV can obtain a large amount of information such as images, which can provide a large amount of data for learning based on vision-based deep learning autonomous navigation methods, which promotes the development of UAV toward autonomous navigation methods [3].…”

Section: Introductionmentioning

confidence: 99%

An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning

Liu

et al. 2022

Wireless Communications and Mobile Computing

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Whether it is for military or civilian use, quadrotor UAV has always been one of research central issues. Most of the current quadrotor drones are manually operated and use GPS signals for navigation, which not only limits the operating range of the drone but also consumes a lot of manpower and material resources. This research mainly studies the method of realizing autonomous flight and conflict avoidance of quadrotor UAV by using multisensor system and deep learning method in extreme flight conditions through track prediction. The convolutional neural network method is used to extract the image information collected by the UAV sensor system. And it uses the cyclic neural network to extract the time feature of the information collected by the UAV sensor. The research results show that the track prediction method based on the deep learning method has higher flight accuracy for quadrotor UAVs. The yaw error of the spatial position is only 2.82%, and the maximum error of the time characteristic error tolerance is only 0.77%.

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Section: Introductionmentioning

confidence: 99%