TY - GEN
T1 - Inverted ant colony optimization for search and rescue in an unknown maze-like indoor environment
AU - Husain, Zainab
AU - Ruta, Dymitr
AU - Sare, Fabrice
AU - Al-Hammadi, Yousof
AU - Isakovic, A. F.
N1 - Funding Information:
We gratefully acknowledge the support from UAE ICT Fund grant on “Biologically Inspired Self-organizing Network Services” and Prof. Sami Muhaidat (KUST) for advices with the models of indoor signal propagation.
Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/7/6
Y1 - 2018/7/6
N2 - We demonstrate the applicability of inverted Ant Colony Optimization (iACO) for target search in a complex unknown indoor environment simulated by a maze. e colony of autonomous ants lay repellent pheromones to speed up exploration of the unknown maze instead of reinforcing presence in already visited areas. e 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 eective one. e presented models can be implemented with self-organizing wireless sensor networks carried by autonomous drones or vehicles and can oer life-saving services of localizing victims of natural disasters or during major infrastructure failures.
AB - We demonstrate the applicability of inverted Ant Colony Optimization (iACO) for target search in a complex unknown indoor environment simulated by a maze. e colony of autonomous ants lay repellent pheromones to speed up exploration of the unknown maze instead of reinforcing presence in already visited areas. e 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 eective one. e presented models can be implemented with self-organizing wireless sensor networks carried by autonomous drones or vehicles and can oer life-saving services of localizing victims of natural disasters or during major infrastructure failures.
UR - https://www.scopus.com/pages/publications/85051557244
U2 - 10.1145/3205651.3205738
DO - 10.1145/3205651.3205738
M3 - Conference contribution
AN - SCOPUS:85051557244
T3 - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
SP - 89
EP - 90
BT - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Y2 - 15 July 2018 through 19 July 2018
ER -