AGV navigation in complex environments: A cooperative trajectory optimization strategy based on SMF-MPC

Zonghe Ding, Ling Chen, Jintao Wang, Qi Qu, Liming Chang and Yajing Cheng

PLOS ONE, 2026, vol. 21, issue 8, 1-29

Abstract: This paper addresses the navigation problem of automated guided vehicles (AGVs) in complex environments by proposing a collaborative trajectory optimization strategy based on the Set Membership Filter (SMF) and Model Predictive Control (MPC). In contrast, the traditional probability distribution-based navigation method, UKF-MPC, often fails to provide a strict safety margin when dealing with UBB noise, thereby increasing the risk of navigation failure. Therefore, this paper proposes a set-membership filter algorithm that constructs a minimum enclosing ellipsoid to enclose the system’s true state in real time and incorporates the geometric characteristics of this ellipsoid as a time-varying penalty term into the MPC optimization objective, thereby achieving dynamic risk avoidance. Finally, comparative experiments under three different types of environmental noise were conducted using MATLAB. The MATLAB simulation results indicate that, under complex environmental disturbances, the SMF-MPC control strategy exhibits a higher navigation success rate and greater robustness than both the traditional UKF-MPC control strategy and the Robust MPC control strategy.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355960 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 55960&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0355960

DOI: 10.1371/journal.pone.0355960

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().