Autonomous Task Allocation for Swarm Robotic Systems Using Behavioral Decomposition

Wei, Yufei; Yasuda, Toshiyuki; Ohkura, Kazuhiro

doi:10.1007/978-3-319-49049-6_34

Cited by 4 publications

(1 citation statement)

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“…In a very large environment, it is relatively inefficient to have just one robot traverse the entire area. Instead, the exploration should be done using a multirobot approach, and swarm robots are used widely for this type of area exploration because of their robustness, flexibility, and scalability [5]. Most existing search methods depend on delicate systems of sensors (e.g., odometers and ultrasound radar) and sophisticated mapping algorithms [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Swarm Robotic Exploration Strategy Based on an Improved Random Walk Method

Pang

Song

Zhang

et al. 2019

Journal of Robotics

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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.

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Section: Introductionmentioning

confidence: 99%