Research on Trajectory Tracking and Obstacle Avoidance Methods for UAV Swarm Based on Model Predictive Control

Haonan Li, Junsong Huang, Leting Wang, Teng Wang, Hairuo Zhang, Xiaoyang Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

UAV swarm systems have been widely used in many fields. When performing formation flight missions in complex airspace environments, UAV swarms often encounter sudden obstacle threats. UAV swarms need to track predetermined trajectories while avoiding sudden obstacles. To address the three-dimensional path tracking control and obstacle avoidance problem of UAV swarms, a nonlinear model predictive control algorithm combined with adaptive artificial potential field method is proposed. A trajectory tracking and obstacle avoidance model for UAV swarms is established, and a cost function considering internal collisions of UAV swarms and external obstacles is designed. The algorithm achieves UAV swarm tracking of predetermined trajectories while maintaining formation and avoiding sudden obstacles. The feasibility and effectiveness of the algorithm are verified through simulation calculations.

Original languageEnglish
Title of host publicationProceedings of 2024 12th China Conference on Command and Control
PublisherSpringer Science and Business Media Deutschland GmbH
Pages351-362
Number of pages12
ISBN (Print)9789819777730
DOIs
StatePublished - 2024
Event12th China Conference on Command and Control, C2 2024 - Beijing, China
Duration: 17 May 202418 May 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1267 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference12th China Conference on Command and Control, C2 2024
Country/TerritoryChina
CityBeijing
Period17/05/2418/05/24

Keywords

  • Adaptive Artificial Potential Field Method
  • Model Predictive Control
  • Obstacle Avoidance
  • Trajectory Tracking
  • UAV Swarm

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