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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 L\u00e9vy 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 L\u00e9vy flight.<\/jats:p>","DOI":"10.1155\/2019\/6914212","type":"journal-article","created":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T19:31:21Z","timestamp":1552505481000},"page":"1-9","source":"Crossref","is-referenced-by-count":39,"title":["A Swarm Robotic Exploration Strategy Based on an Improved Random Walk Method"],"prefix":"10.1155","volume":"2019","author":[{"given":"Bao","family":"Pang","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2505-2766","authenticated-orcid":true,"given":"Yong","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electrical and Information Engineering, Shandong University at 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