Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions

De Rango, Floriano, Palmieri, Nunzia, Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556 and Marano, Salvatore (2015) Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions. In: SPECTS 2015 : International Symposium on Performance Evaluation of Computer and Telecommunication Systems, 26-29 Jul 2015, Chicago, IL, USA.

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Abstract

In this paper, the problem of coverage and exploration of unknown and mined spaces is investigated using a team of robots. The goal is to propose a strategy capable to minimize the overall exploration and mine disarming time, while avoiding that robots pass many times through the same places. The key problem is that the robots simultaneously have to explore different regions of the environment and for this reason they should spread among the search areas. However, at the same time, when a mine is discovered, more robots are needed to be engaged in order to disarm the mine. Because the problem of the unknown lands with the constraint to disarm mine is a NP hard problem, we proposed a combined approach using two bio-inspired meta-heuristic approaches such as Ant Colony Optimization (ACO) and Firefly algorithm (FA) to perform the coordination task among robots. We have compared the simulation results considering a common exploration task of the robot spreading and an ACO based robot recruiting(ATS-RR) and Firefly inspired (FTS-RR) strategies to perform the mine disarming task. Performance has been evaluated in terms of both overall exploring time and mine disarming time and in terms of number of accesses distributed in the operative grid area. The results show that the combined approach provides a better tool for both exploration and disarmament.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19352
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 19 Apr 2016 10:30
Last Modified: 10 Jun 2019 13:07
URI: https://eprints.mdx.ac.uk/id/eprint/19352

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