Authors:
Nunzia Palmieri
1
;
Floriano de Rango
2
;
Xin She Yang
3
and
Salvatore Marano
2
Affiliations:
1
University of Calabria and Middlesex University, Italy
;
2
University of Calabria, Italy
;
3
Middlesex University, United Kingdom
Keyword(s):
Swarm Intelligence, Swarm Robotics, Firefly Algorithm, Bio-Inspired Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Evolutionary Robotics and Intelligent Agents
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
In this paper, two metaheuristics are presented for exploration and mine disarming tasks performed by a
swarm of robots. The objective is to explore autonomously an unknown area in order to discover the mines,
disseminated in the area, and disarm them in cooperative manner since a mine needs multiple robots to disarm.
The problem is bi-objective: distributing in different regions the robots in order to explore the area in a
minimum amount of time and recruiting the robots in the same location to disarm the mines. While
autonomous exploration has been investigated in the past, we specifically focus on the issue of how the swarm
can inform its members about the detected mines, and guide robots to the locations. We propose two bio-inspired
strategies to coordinate the swarm: the first is based on the Ant Colony Optimization (ATS-RR) and
the other is based on the Firefly Algorithm (FTS-RR). Our experiments were conducted by simulations
evaluating the performance in terms of exploring and
disarming time and the number of accesses in the
operative grid area applying both strategies in comparison with the Particle Swarm Optimization (PSO). The
results show that FTS-RR strategy performs better especially when the complexity of the tasks increases.
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