Realization of an artificial pheromone system in random data carriers using RFID tags for autonomous navigation

Herianto, -; Kurabayashi, Daisuke

doi:10.1109/robot.2009.5152405

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…Smart environments may be difficult to install and use for real applications; rather, such setups are often employed for targeted research experiments. This category can be further divided into three classes: the usage of (i) radio-frequency identification (RFID) tags Zambonelli 2005, 2007;Herianto et al 2007;Herianto and Kurabayashi 2009;Bosien et al 2012;Khaliq et al 2014); (ii) simulated pheromone environments, using projected light or other custom hardware for virtual pheromone implementations (Sugawara et al 2004;Garnier et al 2007Garnier et al , 2013Arvin et al 2015;Valentini et al 2018) , and (iii) augmented reality tools in which a virtual environment is sensed and acted on by robots using virtual sensors and actuators (Reina et al 2015b(Reina et al , 2017.…”

Section: Stigmergy-based Foraging In Swarm Roboticsmentioning

confidence: 99%

Sophisticated collective foraging with minimalist agents: a swarm robotics test

et al. 2019

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How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

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Section: Stigmergy-based Foraging In Swarm Roboticsmentioning

confidence: 99%

Sophisticated collective foraging with minimalist agents: a swarm robotics test

et al. 2019

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“…There are also works in which the environment is enhanced in order to let it store information. For example, Mamei and Zambonelli (2005) and Herianto and Kurabayashi (2009) deploy RFID tags in the environment so that robots can read from or write in them. Mayet et al (2010) use an environment whose floor is covered with a paint that glows if robots activate ultraviolet LEDs.…”

Section: Collective Decision-making In Artificial Swarmsmentioning

confidence: 99%

Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making

Ferrante

Scheidler

et al. 2011

Swarm Intell

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Collective decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a collective decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism allows a swarm composed of robots with no explicit knowledge about the difference in execution times between the two actions to choose the one with the shorter execution time. We use an opinion formation model that captures important elements of the scenarios in which the proposed mechanism can be used in order to predict the system's behavior. The model predicts that when the two actions have different average execution times, the swarm chooses with high probability the action with the shorter average execution time. We validate the model's predictions through a swarm robotics experiment in which robot teams Electronic supplementary material The online version of this article (306 Swarm Intell (2011) 5:305-327 must choose one of two paths of different length that connect two locations. Thanks to the proposed mechanism, a swarm made of robot teams that do not measure time or distance is able to choose the shorter path.

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“…Several studies, similarly to ours, implemented pheromone communication through a smart environment which was capable to store virtual pheromone information and to provide this information in real-time to the robots [54,18,21,17,1,58]. Within this category, several studies implemented virtual pheromone through the use of RFID tags which were deployed in the environment and stored pheromone information [32,33,24,23,3,29]. Our study relies on a different form of smart environment: Kilobots perceive and deposit virtual pheromone via ARK which has similarities with implementations of [54,18,17,1].…”

Section: Related Workmentioning

confidence: 99%

Quality-Sensitive Foraging by a Robot Swarm Through Virtual Pheromone Trails

Llenas

Talamali

et al. 2018

Lecture Notes in Computer Science

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Large swarms of simple autonomous robots can be employed to find objects clustered at random locations, and transport them to a central depot. This solution offers system parallelisation through concurrent environment exploration and object collection by several robots, but it also introduces the challenge of robot coordination. Inspired by ants' foraging behaviour, we successfully tackle robot swarm coordination through indirect stigmergic communication in the form of virtual pheromone trails. We design and implement a robot swarm composed of up to 100 Kilobots using the recent technology Augmented Reality for Kilobots (ARK). Using pheromone trails, our memoryless robots rediscover object sources that have been located previously. The emerging collective dynamics show a throughput inversely proportional to the source distance. We assume environments with multiple sources, each providing objects of different qualities, and we investigate how the robot swarm balances the quality-distance trade-off by using quality-sensitive pheromone trails. To our knowledge this work represents the largest robotic experiment in stigmergic foraging, and is the first complete demonstration of ARK, showcasing the set of unique functionalities it provides.

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Realization of an artificial pheromone system in random data carriers using RFID tags for autonomous navigation

Cited by 16 publications

References 18 publications

Sophisticated collective foraging with minimalist agents: a swarm robotics test

Sophisticated collective foraging with minimalist agents: a swarm robotics test

Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making

Quality-Sensitive Foraging by a Robot Swarm Through Virtual Pheromone Trails

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