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

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.

I. Introduction

Information warfare will be the main form of a battle in the future. In recent decades, with the great development of artificial intelligence technology, academic communities have been providing some vital technical support for UAV's autonomous flight. At present, UAV is developing in different directions, including intellectualization, invisibility, digitization and miniaturization. During the revolutionary process of the intellectualization of UAV, one of the core technologies is the UAV path planning technology.