Title: A swarm intelligence labour division approach to solving complex area coverage problems of swarm robots

Authors: Renbin Xiao; Husheng Wu; Liang Hu; Jinqiang Hu

Addresses: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China ' School of Equipment Management and Support, Armed Police Force Engineering University, Xi'an, Shanxi 710086, China ' School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China ' School of Equipment Management and Support, Armed Police Force Engineering University, Xi'an, Shanxi 710086, China

Abstract: The complex area coverage problem is classical and widespread in the research field of swarm robots. In order to solve the complex area coverage problem with complex nonlinear boundary and special task area (forbidden area or threat area), firstly, the task area is adjusted and grid discretisation. Then, inspired by the labour division phenomenon of typical biological groups such as bee colony and ant colony, the paper analyses the performance characteristics of typical ant colony labour division model (response threshold model) and bee colony labour division model (activation-inhibition model) from the perspectives of individual and environment, individual and individual, and a new swarm intelligence labour division approach (activation-inhibition response threshold algorithm) to solve the complex area coverage problem of swarm robot. Three experiments are carried out to illustrate that the algorithm are endowed with great ability of area coverage and dynamic environment. It can respond to the sudden threat in time and make an efficient response, which has a good practical application prospects.

Keywords: area coverage; swarm robot; swarm intelligence; labour division; response threshold model; activation-inhibition model.

DOI: 10.1504/IJBIC.2020.108598

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.4, pp.224 - 238

Received: 08 Feb 2020
Accepted: 01 Mar 2020

Published online: 15 Jul 2020 *

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