Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms

Zainab Husain, Amna Al Zaabi, Hanno Hildmann, Fabrice Saffre, Dymitr Ruta, A. F. Isakovic (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

Original languageEnglish
Article number273
JournalDrones
Volume6
Issue number10
DOIs
Publication statusPublished - Oct 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • ACO
  • ant algorithms
  • ant colony optimization
  • civil security
  • drones
  • maze exploration
  • public safety
  • SAR
  • search and rescue
  • smart city
  • UAS
  • UAV

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