Path planning algorithm of power inspection UAV based on multi-modal data

The aim of this research is to design and optimize the path planning algorithm of power inspection UAV by integrating multi-modal data, such as visual information, LiDAR data, GIS and historical fault data. The algorithm uses deep learning and reinforcement learning technology to dynamically sense the power grid equipment and its surrounding environment to achieve an optimal balance between obstacle avoidance and inspection efficiency. Combined with multi-source data, the algorithm can adjust the flight path in real time according to environmental changes to ensure the autonomy and efficiency of the UAV. At the same time, the algorithm introduces a multi-objective optimization strategy, taking into account the accuracy of inspection tasks, path length, energy consumption and other constraints, so as to improve the intelligence and reliability of inspection tasks.