Unmanned aerial vehicle navigation and path planning method based on deep learning

The invention relates to the technical field of unmanned aerial vehicle navigation and path planning, and discloses an unmanned aerial vehicle navigation and path planning method based on deep learning, and the method comprises the following steps: S1, introducing a virtual obstacle in an unmanned aerial vehicle flight environment, and considering the dynamics, diversity and complexity of the obstacle; s2, generating an enhanced data set according to the virtual obstacle, and considering the diversity, balance and robustness of the data set; and S3, collecting sensor data of the flight environment of the unmanned aerial vehicle, including image data, laser radar data, inertial measurement unit data and global positioning system data, and considering fusion of multi-source data. By introducing the virtual obstacle and considering the dynamic nature, diversity and complexity of the virtual obstacle, a real flight environment can be simulated more accurately, and the robustness and safety of path planning are im