Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

Hongchao Zhao,Jianzhong Zhao,J Zhu

doi:10.1051/matecconf/202235503002

Abstract

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

Highlights

Due to their agility and good ability, unmanned aerial vehicles (UAVs) are widely used in both military and civilian fields
In this paper the improved scheme of ant colony algorithm (ACA) is proposed from the aspects of simplicity and effectiveness, in order to overcome the disadvantages of the conventional ACA
The flight performance constraints of fixed wing UAVs are treated as conditions of judging whether the candidate expanded nodes are feasible, which reduces the feasible nodes’ number and raises the search efficiency

Summary

Introduction

Due to their agility and good ability, unmanned aerial vehicles (UAVs) are widely used in both military and civilian fields. Some improved schemes are presented by scholars, such as multi-colony ant optimization [2], hybrid algorithm of ACA and GA [3], cellular ant colony algorithm [4], combining ACA with artificial potential field [5, 6], improved chaotic ant colony algorithm [7], quantum ant colony algorithm [8], etc. Both local pheromone and global pheromone were updated, and heuristic function was modified in [9, 10], which improved the global search ability of ACA.

Description of UAV flight environment

Improved pheromone update rule

Improved heuristic function

Simulation analysis

Conclusions