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

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