Unmanned aerial vehicle optimal path planning method based on adaptive ant colony algorithm

Abstract

The invention discloses an optimal path planning method for an unmanned aerial vehicle based on an adaptive ant colony algorithm. First, the flight area is gridded, parameters are initialized, and ants are placed at the starting point of the grid; and then each ant is moved to a feasible path. Point until all ants reach the target point, calculate the cost of the feasible path and find out the optimal path of this cycle; then update the information and make adaptive adjustments to ρ; finally output the optimal path. The present invention can effectively improve the success rate of UAVs when performing defense penetration reconnaissance missions. Before performing reconnaissance missions in the enemy's defense area, an efficient UAV flight path must be planned and designed to ensure that UAVs can be minimized Discovery probability and optimal path to reach the target point. According to the enemy defense, by adjusting the pheromone volatilization factor, an adaptive ant colony algorithm suitable for path planning is used. The optimal path length can be obtained by using the adaptive ant colony algorithm, and the algorithm convergence time is significantly shortened compared with other methods.

Description

technical field [0001] The invention belongs to the field of information technology, and relates to an optimal path planning method for an unmanned aerial vehicle, in particular to an optimal path planning method for an unmanned aerial vehicle based on an adaptive ant colony algorithm. Background technique [0002] In recent years, the development of drones has been very rapid, and various types of drones have appeared one after another. However, the path planning of drones has many deficiencies in the following aspects: [0003] Scholars at home and abroad mainly use optimization algorithms to solve the UAV path planning problem. Some of them use genetic algorithms, but genetic algorithms still have "premature" phenomenon in the process of searching paths and take a long time to plan paths. There are also some that use the A* algorithm, but there are also problems such as slow search speed and large memory space during operation; a series of solutions have appeared for the ...

AI Technical Summary

Problems solved by technology

[0003] Scholars at home and abroad mainly use optimization algorithms to solve the problem of UAV path planning.

There are also some that use the A* algorithm, but there are also problems such as slow search speed and large memory space during operation; a series of solutions have appeared for the above defects, and the theory used in it can be summarized in detail as follows:

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