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Partial Swarm SLAM for Intelligent Navigation

EasyChair Preprint no. 7965, version 2

Versions: 12history
12 pagesDate: June 4, 2022


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.

Keyphrases: Exploration schemes, multi-agent systems, SLAM, Swarm Intelligence

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jawad Yasin and Huma Mahboob and Suvi Jokinen and Mohammadhashem Haghbayan and Muhammad Mehboob Yasin and Juha Plosila},
  title = {Partial Swarm SLAM for Intelligent Navigation},
  howpublished = {EasyChair Preprint no. 7965},

  year = {EasyChair, 2022}}
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