Inverted ant colony optimization for search and rescue in an unknown maze-like indoor environment.

We demonstrate the applicability of inverted Ant Colony Optimization (iACO) for target search in a complex unknown indoor environment simulated by a maze. The colony of autonomous ants lay repellent pheromones to speed up exploration of the unknown maze instead of reinforcing presence in already visited areas. The role of a target-collocated beacon signal within the maze is evaluated in terms of its utility to guide the search. Variants of iACO were developed, with beacon initialization (iACO-B), and with increased sensing ranges (iACO-R with a 2-step far-sightedness) to quantify the most effective one. The presented models can be implemented with self-organizing wireless sensor networks carried by autonomous drones or vehicles and can offer life-saving services of localizing victims of natural disasters or during major infrastructure failures.