research on UAV-assisted path planning system based on deep learning
Caiyun Wu, Yu Gong, Fan Bai · 2023
With the widespread use of First Person View (FPV) Drone in film and television shooting, terrain reconnaissance, etc, the operator simultaneously handles the target task and obstacle avoidance trajectory planning flight task under greater pressure, which is very likely to lead to accidents, so combining monocular visual depth-of-field algorithm with Unmanned Aerial Vehicle(UAV) dynamic information to provide the controller with trajectory-assisted indication during flight becomes the focus of this research paper. In this paper, by combining the artificial potential field with deep learning, we use deep learning to regress the target motion trajectory under the action of depth-of-field potential, effectively predict multiple possible obstacle avoidance trajectories under the current position by introducing UAV positional information and UAV viewpoint images, improve the computational speed by streamlining the network, and deploy the algorithm model to Jetson TX2 for experimental validation. The experimental results show that the channel detection achieves the design requirements and realizes the effect of UAV aircraft to assist the controller in high dynamic scenes.