A4 Refereed article in a conference publication
Partial Swarm SLAM for Intelligent Navigation
Authors: Yasin Jawad N., Mahboob Huma, Jokinen Suvi, Haghbayan Hashem, Yasin Muhammad Mehboob, Plosila Juha
Editors: Frank Dignum, Philippe Mathieu, Juan Manuel Corchado, Fernando De La Prieta
Conference name: International Conference on Practical Applications of Agents and Multi-Agent Systems
Publisher: Springer Science and Business Media Deutschland GmbH
Publishing place: Cham
Publication year: 2022
Journal: Lecture Notes in Computer Science
Book title : Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection: 20th International Conference, PAAMS 2022, L'Aquila, Italy, July 13–15, 2022, Proceedings
Journal name in source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Series title: Lecture Notes in Computer Science
Volume: 13616
First page : 435
Last page: 446
ISBN: 978-3-031-18191-7
eISBN: 978-3-031-18192-4
ISSN: 0302-9743
eISSN: 1611-3349
DOI: https://doi.org/10.1007/978-3-031-18192-4_35
Web address : https://link.springer.com/chapter/10.1007/978-3-031-18192-4_35
The focus of this work is to present a novel methodology utilizing the classical SLAM technique and integrating with the swarm agents for localizing, guiding, and retrieving the agents towards the optimal path while using only necessary tracker-based information between the agents. While navigating in an unknown environment with no-prior map information, upon encountering large obstacles (out of the field of view detection range of the onboard sensors, the swarm is divided into sub-swarms. This is done while dropping tracking points at every turn. Similarly, the time stamps for every turn taken and the gap width available between obstacles are recorded. Once an agent from any sub-swarm category reaches the destination, the agent broadcasts these tracker points to the rest of the swarm agents. Utilizing this broadcasted key information, the rest of the agents are able to navigate toward the destination without having to find the path. With the help of simulation examples, it is shown that the proposed technique is efficient over other similar randomized turn-based techniques.
