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

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.

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

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Constructing a cohesive pattern for collective navigation based on a swarm of robotics

“…In swarm robotics, every robot has a unique ID (Robot ID) (Hu et al, 2014). Due to the limited capability of Kilobot robots, we use the random walk algorithm in this phase due to its simplicity (Pang et al, 2019). Besides, the random walk is used to characterize insects' movement distinguished by a sequence of straight motion and turns (Wang et al, 2020).…”

The Effect of Obstacle Design Architectures on Randomly Ranging AGVs in a Shared Workspace

“…Examples of models based on analytical models include Gaussian distribution [34], [35], [42], random tree [35], and Monte Carlo sampling strategies [34] [36]. Current research [35], [36], [38], [44]- [46] indicates that the focus is primarily on the improvement of robotic path planning using different mathematical models. However, there is no substantial evidence of any mathematical form that can be used to calculate a process of creating random motion such as Simple Random Walk (SRW) for studying the effect of static and dynamic obstacles.…”