Formation Control of Wheeled Mobile Robots with Fault-Tolerance Capabilities

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1 Scopus citations

Abstract

This research investigates the impact of actuator faults on the formation control of multiple-wheeled mobile robots—a critical aspect in coordinating multi-robot systems for applications such as surveillance, exploration, and transportation. When a fault occurs in any of the robots, it can disrupt the formation and adversely affect the system’s performance, thereby compromising system efficiency and reliability. While numerous studies have focused on fault-tolerant control strategies to maintain formation integrity, there is a notable gap in the literature regarding the relationship between controller gains and settling time under varying degrees of actuator loss. In this paper, we develop a kinematic model of wheeled mobile robots and implement a leader–follower-based formation control strategy. Actuator faults are systematically introduced with varying levels of effectiveness (e.g., 80%, 60%, and 40% of full capacity) to observe their effects on formation maintenance. We generate data correlating controller gains with settling time under different actuator loss conditions and fit a polynomial curve to derive an equation describing this relationship. Comprehensive MATLAB simulations are conducted to evaluate the proposed methodology. The results demonstrate the influence of actuator faults on the formation control system and provide valuable insights into optimizing controller gains for improved fault tolerance. These findings contribute to the development of more robust multi-robot systems capable of maintaining formation and performance despite the presence of actuator failures.

Original languageEnglish
Article number59
JournalRobotics
Volume14
Issue number5
DOIs
StatePublished - May 2025

Bibliographical note

Publisher Copyright:
© 2025 by the authors.

Keywords

  • actuator effectiveness
  • formation control
  • multi-agent systems
  • polynomial curve fitting

ASJC Scopus subject areas

  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence

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