Open AccessAn Improved Genetic Algorithm for Rapid UAV Path Planning
Author(s) -
Jinrong Liu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012035
Subject(s) - motion planning , robustness (evolution) , fitness function , genetic algorithm , computer science , obstacle , swarm behaviour , path (computing) , mathematical optimization , algorithm , real time computing , artificial intelligence , mathematics , robot , machine learning , geography , biochemistry , chemistry , archaeology , gene , programming language
UAV technologies have advanced rapidly and are widely used in military and civilian fields. For instance, the UAV swarms have been widely applied to oil and gas exploration, geometric mapping, and cargo transport. However, the UAV swarm system requires a more accurate plan before performing any mission. Path planning is one of the essential parts of mission planning because of the higher requirement of robustness and real-time communication. UAV path planning could generate the optimal path starting from the current position to the target in an environment with an obstacle. While the standard genetic algorithm has lacked efficiency in the iteration process and poor stability, a new genetic operator is proposed for the genetic algorithm and applied to the path planning simulation of UAV swarms in this study. A three-dimensional mapping and fitness function are already constructed for the simulation. The simulation result shows the algorithm with improved selection and mutation operator can efficiently and stably converge to the optimal solution.