Efficient spatial coverage by a robot swarm based on an ant foraging model and the Lévy distribution

Schroeder, Adam; Ramakrishnan, Suresh; Kumar, Manish; Trease, Brian P.

doi:10.1007/s11721-017-0132-y

Cited by 41 publications

(26 citation statements)

References 45 publications

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“…For example, Dimidov et al [3] studied the performances of different variants of random walk while exploring and searching for a static target in the environment. Schroeder et al [24] conducted a similar research and compared different variants of random walk to cover the environment. The results of these studies highlight the relationship between the performance on the task and the ability of the swarm to explore the environment with a particular variant.…”

Section: Related Workmentioning

confidence: 99%

Random Walk Exploration for Swarm Mapping

Kegeleirs

Ramos

Birattari

2019

Lecture Notes in Computer Science

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Miquel Kegeleirs [0000−0002−9018−4995] , David Garzón Ramos [0000−0001−7099−4213] , and Mauro Birattari [0000−0003−3309−2194] Abstract. Research in swarm robotics has shown that robot swarms are effective in the exploration of unknown environments. However, little work has been devoted to port the exploration capabilities of robot swarms into the context of mapping. Indeed, conceiving robot swarms that can map an unknown environment in a robust, scalable, and flexible way is an open issue. In this paper, we investigate a swarm mapping method in which robots first individually map the environment by random walk and then, we merge their maps into a single, global one. We focus on five variants of random walk and we compare the quality of the maps that a swarm produces when exploring the environment using these variants. Our experiments with ten e-puck robots show that, despite the individual maps being incomplete by themselves, it is possible to collectively map the environment by merging them. We found that the quality of the map depends on the exploration behavior of the individuals. Our results suggest that one of the variants of random walk, the ballistic motion, gives better mapping results for closed environments.

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Section: Related Workmentioning

confidence: 99%

Random Walk Exploration for Swarm Mapping

Kegeleirs

Ramos

Birattari

2019

Lecture Notes in Computer Science

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“…Refined local control laws, which give rise to a desired distribution of robots, are a main topic of current research in particle swarm optimization. Biologically motivated strategies include the directed movement driven by a chemical cue, chemotaxis, which is used to devise efficient search strategies for Lévy robotic systems with sensing capabilities [37]. In bacterial foraging algorithms the length of the run or the tumbling may depend on external cues, such as pheromones, similarly leading to chemotactic behavior [45].…”

Section: Macroscopic Transport Equation For Swarmingmentioning

confidence: 99%

“…Lévy movement directed by external cues, such as chemotaxis, has been studied in [33] to find a contaminant in water, while [31] considers sonotaxis. Efficient spatial coverage in the presence of pheromone cues was specifically addressed in [37], using an ant-inspired search strategy with long range movement.…”

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confidence: 99%

Interacting Particles with Lévy Strategies: Limits of Transport Equations for Swarm Robotic Systems

Estrada-Rodriguez¹,

Gimperlein²

2020

SIAM J. Appl. Math.

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Lévy robotic systems combine superdiffusive random movement with emergent collective behaviour from local communication and alignment in order to find rare targets or track objects. In this article we derive macroscopic fractional PDE descriptions from the movement strategies of the individual robots. Starting from a kinetic equation which describes the movement of robots based on alignment, collisions and occasional long distance runs according to a Lévy distribution, we obtain a system of evolution equations for the fractional diffusion for long times. We show that the system allows efficient parameter studies for a search problem, addressing basic questions like the optimal number of robots needed to cover an area in a certain time. For shorter times, in the hyperbolic limit of the kinetic equation, the PDE model is dominated by alignment, irrespective of the long range movement. This is in agreement with previous results in swarming of self-propelled particles. The article indicates the novel and quantitative modeling opportunities which swarm robotic systems provide for the study of both emergent collective behaviour and anomalous diffusion, on the respective time scales.

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“…Fujisawa and Dobata showed that the LF search strategy maximized the SE of swarm robots by using pheromone to communicate with each other [23]. Schroeder et al proposed a control law that combines a virtual pheromone and LF for efficient area coverage [24].…”

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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Efficient spatial coverage by a robot swarm based on an ant foraging model and the Lévy distribution

Cited by 41 publications

References 45 publications

Random Walk Exploration for Swarm Mapping

Random Walk Exploration for Swarm Mapping

Interacting Particles with Lévy Strategies: Limits of Transport Equations for Swarm Robotic Systems

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

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