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Copyright © 2019 Bao Pang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/

Abstract

An environment can be searched far more efficiently if the appropriate search strategy is used. Because of the limited individual abilities of swarm robots, namely, local sensing and low processing power, random searching is the main search strategy used in swarm robotics. The random walk methods that are used most commonly are Brownian motion and Lévy flight, both of which mimic the self-organized behavior of social insects. However, both methods are somewhat limited when applied to swarm robotics, where having the robots search repeatedly can result in highly inefficient searching. Therefore, by analyzing the characteristics of swarm robotic exploration, this paper proposes an improved random walk method in which each robot adjusts its step size adaptively to reduce the number of repeated searches by estimating the density of robots in the environment. Simulation experiments and experiments with actual robots are conducted to study the effectiveness of the proposed method and evaluate its performance in an exploration mission. The experimental results presented in this paper show that an area is covered more efficiently using the proposed method than it is using either Brownian motion or Lévy flight.

Details

Title
A Swarm Robotic Exploration Strategy Based on an Improved Random Walk Method
Author
Pang, Bao 1 ; Song, Yong 2   VIAFID ORCID Logo  ; Zhang, Chengjin 3   VIAFID ORCID Logo  ; Wang, Hongling 1 ; Yang, Runtao 2   VIAFID ORCID Logo 

 School of Control Science and Engineering, Shandong University, Jinan 250061, China 
 School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai 264209, China 
 School of Control Science and Engineering, Shandong University, Jinan 250061, China; School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai 264209, China 
Editor
Shahram Payandeh
Publication year
2019
Publication date
2019
Publisher
John Wiley & Sons, Inc.
ISSN
16879600
e-ISSN
16879619
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2196464731
Copyright
Copyright © 2019 Bao Pang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/