Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques

Nunzia Palmieri, Floriano de Rango, Xin She Yang, Salvatore Marano

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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Paper Citation


in Harvard Style

Palmieri N., de Rango F., She Yang X. and Marano S. (2015). Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 74-82. DOI: 10.5220/0005596200740082


in Bibtex Style

@conference{ecta15,
author={Nunzia Palmieri and Floriano de Rango and Xin She Yang and Salvatore Marano},
title={Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={74-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005596200740082},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques
SN - 978-989-758-157-1
AU - Palmieri N.
AU - de Rango F.
AU - She Yang X.
AU - Marano S.
PY - 2015
SP - 74
EP - 82
DO - 10.5220/0005596200740082