Efficient relative localization and coordination system for unmanned ground vehicle formations under directed graph structure


Kabore K. M., Güler S.

Robotica, vol.43, no.4, pp.1199-1221, 2025 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 4
  • Publication Date: 2025
  • Doi Number: 10.1017/s026357472500013x
  • Journal Name: Robotica
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.1199-1221
  • Keywords: convolutional neural networks, formation control, mobile robotics, relative localization
  • Abdullah Gül University Affiliated: Yes

Abstract

Onboard localization for multi-robot systems stands as a critical area of research with wide-ranging applications. This paper introduces an innovative framework for multi-robot localization, uniquely characterized by its onboard capability, thereby negating the dependency on external infrastructure. Our approach harnesses the inherent capabilities of each robot, enabling them to localize and synchronize their movements independently. The integration of cooperative localization algorithms with formation control mechanisms empowers a group of robots to sustain a predefined formation while following a linear trajectory. The efficacy of our framework is substantiated through comprehensive simulations and real-world experimental validations. We rigorously assess the system’s resilience to localization inaccuracies and external disturbances, demonstrating its adaptability and consistency in maintaining formation under diverse conditions. Furthermore, we explore the scalability of our approach, highlighting its potential to manage varying numbers of robots and its applicability in tasks such as collaborative transportation.