Colias-Φ: An Autonomous Micro Robot for Artificial Pheromone Communication

Arvin, Farshad; Xiong, Caihua; Yue, Shigang

doi:10.18178/ijmerr.4.4.349-353

Cited by 14 publications

(12 citation statements)

References 23 publications

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“…Perhaps the most popular approach in implementing pheromone communication is through a smart environment (Sugawara et al 2004;Garnier et al 2007;Hecker et al 2012;Garnier et al 2013;Arvin et al 2015;Valentini et al 2018), which has the capability to store and to supply virtual pheromone information to robot agents in real-time. The popularity arises from the fact that this approach is generally low cost and easily adaptable to different sizes of swarm and environment.…”

Section: Stigmergy-based Foraging In Swarm Roboticsmentioning

confidence: 99%

“…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%

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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%

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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“…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].…”

Section: Related Workmentioning

confidence: 99%

“…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]. Similarly to ARK, robots were real-time tracked with an overhead camera, although, differently from ARK, their robots used the light sensors to read the virtual pheromone that was projected as light on the floor.…”

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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“…In all these works, the robots' locations are tracked through overhead cameras connected to a base control station which then delivers virtual environment information to the robots. In most works, the system is designed for a specific use case (rather than as a general purpose tool); for example, to generate virtual pheromone trails that are either projected on the ground through an overhead projector [14], [16] or displayed on an LCD screen which acts as the robots' ground [21]. Alternatively in [17], [19], robots can sense any type of virtual environment.…”

Section: Related Workmentioning

confidence: 99%

ARK: Augmented Reality for Kilobots

Reina

Cope

Nikolaidis³

et al. 2017

IEEE Robot. Autom. Lett.

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Abstract-Working with large swarms of robots has challenges in calibration, sensing, tracking, and control due to the associated scalability and time requirements. Kilobots solve this through their ease of maintenance and programming, and are widely used in several research laboratories worldwide where their low cost enables large-scale swarms studies. However, the small, inexpensive nature of the Kilobots limits their range of capabilities as they are only equipped with a single sensor. In some studies, this limitation can be a source of motivation and inspiration, while in others it is an impediment. As such, we designed, implemented, and tested a novel system to communicate personalised location-and-state-based information to each robot, and receive information on each robots' state. In this way, the Kilobots can sense additional information from a virtual environment in real-time; for example, a value on a gradient, a direction towards a reference point or a pheromone trail. The Augmented Reality for Kilobots (ARK) system implements this in flexible base control software which allows users to define varying virtual environments within a single experiment using integrated overhead tracking and control. We showcase the different functionalities of the system through three demos involving hundreds of Kilobots. The ARK provides Kilobots with additional and unique capabilities through an open-source tool which can be implemented with inexpensive, off-the-shelf hardware.

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Colias-Φ: An Autonomous Micro Robot for Artificial Pheromone Communication

Cited by 14 publications

References 23 publications

Sophisticated collective foraging with minimalist agents: a swarm robotics test

Sophisticated collective foraging with minimalist agents: a swarm robotics test

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

ARK: Augmented Reality for Kilobots

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