Autonomous Forklifts: State of the Art—Exploring Perception, Scanning Technologies and Functional Systems—A Comprehensive Review

Muftah A. Fraifer, Joseph Coleman, James Maguire, Petar Trslić, Gerard Dooly, Daniel Toal

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a comprehensive overview of cutting-edge autonomous forklifts, with a strong emphasis on sensors, object detection and system functionality. It aims to explore how this technology is evolving and where it is likely headed in both the near and long-term future, while also highlighting the latest developments in both academic research and industrial applications. Given the critical importance of object detection and recognition in machine vision and autonomous vehicles, this area receives particular attention. The article provides an in-depth summary of both commercial and prototype forklifts, discussing key aspects such as design features, capabilities and benefits, and offers a detailed technical comparison. Specifically, it clarifies that all available data pertains to commercially available forklifts. To obtain a better understanding of the current state-of-the-art and its limitations, the analysis also reviews commercially available autonomous forklifts. Finally, this paper includes a comprehensive bibliography of research findings in this field.

Original languageEnglish
Article number153
JournalElectronics (Switzerland)
Volume14
Issue number1
DOIs
Publication statusPublished - Jan 2025

Keywords

  • automated guided vehicle
  • autonomous forklift
  • LiDAR
  • machine vision models
  • object detection
  • sensors
  • time of flight (ToF)

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