Path planning of UAV based on hierarchical genetic algorithm with optimized search region

Abstract

In general, the use of genetic algorithms (GA) for unmanned aerial vehicle (UAV) path planning in the whole mission area will cause detours. To improve this issue, a hierarchical genetic algorithm with optimized search region (OSR-HGA) is proposed. This algorithm reduces the search area of hierarchical genetic algorithm automatically by evaluating the distribution of threat sources in the mission area. To guide the searching direction of the algorithm and reduce the occurrence of detours, the heading correction cost and minimum turning radius cost are added to the cost function. The experimental results show the new method can enhance the stability of path planning algorithm by finding shorter paths with less cost and reducing the occurrence of detours effectively.