Research and Verification of Multi-UAV Formation Control Based on Grey Wolf Optimization Algorithm

With the rapid development of intelligent and unmanned technology, the demand for unmanned equipment in military, electric power, survey and other fields has gradually increased. UAV has unique advantages such as low cost, high flexibility and strong expansibility, and has strong application value in military and industrial fields. However, the tasks that a single UAV can accomplish are limited. In order to better accomplish more complex and intelligent tasks, multi-UAV cooperation under swarm intelligence has become a hot research topic. In this paper, the formation control method of UAV cluster based on grey wolf optimization algorithm is studied. The flight environment and trajectory are modeled and the control algorithm is programmed in Python language. Secondly, based on the Airsim UAV simulation platform, the UAV cluster formation control algorithm is simulated and verified. Finally, nine four-rotor UAVs based on Pixhawk flight control and raspberry pie airborne computer are used to verify the intelligent formation control algorithm adopted in this paper. Through simulation and actual flight verification, the cluster control algorithm adopted in this paper can make the UAV cluster fly safely and smoothly in accordance with the preset route in space.

Authors

Institutions

Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/icus61736.2024.10839868
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research and Verification of Multi-UAV Formation Control Based on Grey Wolf Optimization Algorithm

Jianfeng Wang, Boya Liu, Jiayu Chen
Robotic Path Planning Algorithms
article

Research and Verification of Multi-UAV Formation Control Based on Grey Wolf Optimization Algorithm

Jianfeng Wang, Boya Liu, Jiayu Chen
article en

Abstract

With the rapid development of intelligent and unmanned technology, the demand for unmanned equipment in military, electric power, survey and other fields has gradually increased. UAV has unique advantages such as low cost, high flexibility and strong expansibility, and has strong application value in military and industrial fields. However, the tasks that a single UAV can accomplish are limited. In order to better accomplish more complex and intelligent tasks, multi-UAV cooperation under swarm intelligence has become a hot research topic. In this paper, the formation control method of UAV cluster based on grey wolf optimization algorithm is studied. The flight environment and trajectory are modeled and the control algorithm is programmed in Python language. Secondly, based on the Airsim UAV simulation platform, the UAV cluster formation control algorithm is simulated and verified. Finally, nine four-rotor UAVs based on Pixhawk flight control and raspberry pie airborne computer are used to verify the intelligent formation control algorithm adopted in this paper. Through simulation and actual flight verification, the cluster control algorithm adopted in this paper can make the UAV cluster fly safely and smoothly in accordance with the preset route in space.

China Aerodynamics Research and Development Center (CN)
Climate action
Openalex Percentile: Top 74%
Robotic Path Planning Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research and Verification of Multi-UAV Formation Control Based on Grey Wolf Optimization Algorithm — Jianfeng Wang, Boya Liu, et al. · (2024) | TGRS Research Map | TGRS