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- W4318605530 abstract "This paper investigates the autonomous landing guidance and control for cooperative unmanned aerial vehicle-unmanned surface vehicle (UAV-USV) systems. A deep learning-based visual positioning architecture is developed to estimate the position of the landing pad on the USV from images captured by the UAV’s onboard camera. By introducing the spatial softmax operation into the conventional convolutional networks, the positioning accuracy and the computational efficiency are improved. Then, a three-stage landing guidance strategy with safety protocols is proposed, and a reinforcement learning-based velocity controller from our earlier work is employed for the UAV motion control. Finally, the proposed approaches are validated in our built simulation platform with challenging landing tasks. The results show that the UAV can safely land on a moving USV under the disturbances of sea waves and wind gusts." @default.
- W4318605530 created "2023-01-31" @default.
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- W4318605530 date "2022-11-19" @default.
- W4318605530 modified "2023-09-23" @default.
- W4318605530 title "Autonomous Landing Guidance and Control for Cooperative UAV-USV Systems" @default.
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- W4318605530 doi "https://doi.org/10.1109/yac57282.2022.10023560" @default.
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