Unmanned aerial vehicle flight path planning method, forest fire prevention system and computer readable storage medium

Abstract

The invention provides an unmanned aerial vehicle flight path planning method, a forest fire prevention system and a computer readable storage medium, and the method comprises the steps: obtaining a plurality of preset pathways, and carrying out the clustering of the plurality of pathways through employing a K clustering algorithm; and respectively adopting a preset genetic algorithm for each cluster to search for the shortest trajectory between the pathways in the cluster, carrying out inter-cluster connection on all the clusters, and merging all the trajectories to obtain a final trajectory planning result. The invention further provides a forest fire prevention system for implementing the method and a computer readable storage medium. According to the invention, the convergence speed of the unmanned aerial vehicle flight path planning algorithm can be accelerated, the flight path planning cost can be saved, and the flight path planning efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of UAV control, in particular, a UAV track planning method, a forest fire prevention system and a computer-readable storage medium for realizing the method. Background technique [0002] Unmanned aircraft are usually called UAVs. With the development of UAV technology, UAVs have been widely used in many fields, such as aerial photography, agriculture, forestry and plant protection, geological exploration, power inspection, oil and gas pipeline inspection , Expressway accident management, forest fire prevention inspection, polluted environment investigation, anti-terrorism and stability maintenance, public security law enforcement, emergency rescue and ambulance, emergency rescue and disaster relief, coastline inspection and other fields. Since UAVs need to fly for a long time and long distances, the autonomous navigation technology of UAVs is particularly important. [0003] The autonomous navigatio...

AI Technical Summary

Problems solved by technology

However, due to the randomness of the existing genetic algorithm solution, the results are fluctuating and the calculation efficiency is low, and with the increase of the data scale, there are great requirements for the calculation time and computing power, resulting in the unmanned aerial vehicles Track planning has problems such as poor adaptability, high complexity, slow convergence speed, and low accuracy. It can be seen that the existing genetic algorithm is not conducive to the navigation and planning of UAVs.

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