@misc{12813,
  abstract     = {{Autonomous Mobile Robots, as the advanced version of Automated Guided Vehicles have received a lot of interest and recognition in recent years. Simultaneous Localization and Mapping (SLAM) techniques enable the vehicles to independently navigate and map their surroundings so that they can drive autonomously in changing and uncharted areas. Due to the increasing importance and contributive development of SLAMs for automated guided vehicles and autonomous mobile robots, this study seeks to provide an in-depth analysis of well-known SLAM techniques developed and applied during the previous ten years. Well-known SLAM algorithms considered in this paper include GMapping, Cartographer, LIO-SAM, and so on. They are mainly examined and compared from the viewpoints of basic principles, sensor requirements, computing complexity, and performance. The aim of this paper is to offer insights into various SLAM approaches to researchers, practitioners, and developers in the field of automated guided vehicles and autonomous mobile robots, facilitating the selection of suitable SLAM methods for specific applications and fostering innovation in autonomous navigation and mapping.}},
  author       = {{Li, Li and Schulze, Lothar and Kalavadia, Kunal Satish}},
  booktitle    = {{5th International Conference on Industry 4.0 and Smart Manufacturing (ISM)}},
  editor       = {{Longo, F. and Shen, W. and Padovano, A.}},
  issn         = {{1877-0509}},
  keywords     = {{Automated Guided Vehicle, Autonomous Mobile Robot, Simultaneous Localization and Mapping, Robot Operating System}},
  location     = {{Lisbon, PORTUGAL}},
  pages        = {{2867--2874}},
  publisher    = {{Elsevier BV}},
  title        = {{{Promising SLAM Methods for Automated Guided Vehicles and Autonomous Mobile Robots}}},
  doi          = {{10.1016/j.procs.2024.02.103}},
  volume       = {{232}},
  year         = {{2024}},
}

@inproceedings{593,
  abstract     = {{Due to the increased individualization of customer demands in the last 20 years, the production systems are required to be more flexible and scalable. It is the samefor the material flow system with automated guided vehicles (AGVs). To realize the flexibility and scalability, it is recommended to decentralized control the vehicles. As an attempt, a concept of swarm intelligence with Radio Frequency Identification (RFID) is proposed and introduced in this article. The concept is supposed to be used for automated guided vehicle systems in which objects have to be transported from place to place. Therefore the object has to be self-organized and has to manage its own transport. In this context the vehicles have to choose the most optimal transportation. Swarm intelligence is a topic which deserves a high level of attention as a method to realize high flexibility and scalability.}},
  author       = {{Cantauw, Alisa Maria and Li, Li}},
  booktitle    = {{Department of Production Engineering and Management}},
  editor       = {{Villmer, Franz-Josef and Padoano, Elio}},
  isbn         = {{978-3-946856-00-9}},
  keywords     = {{Swarm  intelligence, Automated  guided  vehicle  system, RFID, Internet  of things, Multi-agent system}},
  location     = {{Lemgo}},
  number       = {{1}},
  pages        = {{133--143}},
  title        = {{{Application of Swarm Intelligence for Automated Guided Vehicle Systems}}},
  year         = {{2016}},
}