ARK: Augmented Reality for Kilobots

In the last few decades we have witnessed how the pheromone of social insect has become a rich inspiration source of swarm robotics. By utilising the virtual pheromone in physical swarm robot system to coordinate individuals and realise direct/indirect inter-robot communications like the social insect, stigmergic behaviour has emerged. However, many studies only take one single pheromone into account in solving swarm problems, which is not the case in real insects. In the real social insect world, diverse behaviours, complex collective performances and flexible transition from one state to another are guided by different kinds of pheromones and their interactions. Therefore, whether multiple pheromone based strategy can inspire swarm robotics research, and inversely how the performances of swarm robots controlled by multiple pheromones bring inspirations to explain the social insects' behaviours will become an interesting question. Thus, to provide a reliable system to undertake the multiple pheromone study, in this paper, we specifically proposed and realised a multiple pheromone communication system called ColCOSΦ. This system consists of a virtual pheromone sub-system wherein the multiple pheromone is represented by a colour image displayed on a screen, and the micro-robots platform designed for swarm robotics applications. Two case studies are undertaken to verify the effectiveness of this system: one is the multiple pheromone based on an ant's forage and another is the interactions of aggregation and alarm pheromones. The experimental results demonstrate the feasibility of ColCOSΦ and its great potential in directing swarm robotics and social insects research.This work was supported by EU Horizon 2020 project STEP2DYNA (691154) and ULTRACEPT (778062).

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“…Yes. Specific data encoding Cannot implement all the properties of pheromone Localisation based virtual environment [21], [22], [23] Yes.…”

Section: Data Informationmentioning

confidence: 99%

ColCOS Φ: A Multiple Pheromone Communication System for Swarm Robotics and Social Insects Research

Sun

Liu

et al. 2019

2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)

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“…Most researchers rely on manual calibration, but this process is cumbersome and dependent on the surface on which the Kilobot maneuver. The ARK platform [18] enables the parallel calibration of tens of robots. While ARK shortens calibration time, it cannot guarantee errorfree precision due to the intrinsically high noise of Kilobot motion.…”

Section: Noise and Inter-individual Variationsmentioning

confidence: 99%

“…The Augmented Reality for Kilobots system (ARK) [18] overcomes the limitations of Kilobots by providing a flexible set of virtual environments, with which BCS Cameras OHC Extra IR light Figure 3. Graphical representation of the ARK architecture.…”

Section: Ark Simulationmentioning

confidence: 99%

“…Each environment has user-defined structure and spatio-temporal dynamics, as exemplified in [6]. As well as enabling richer experimental paradigms, ARK's features also lend it to automatic motor calibration and other house-keeping features [18]. ARK is integrated with ARGoS through the ARK Loop Function (ALF), the simulated counterpart of the ARK's base control station.…”

Section: Ark Simulationmentioning

confidence: 99%

“…Meeting these design goals meant sacrificing important features for swarm robotics research, such as accurate environmental sensing and remote access to the robot state. To compensate for missing features, devices such as the Kilogrid [24] and the ARK virtualisation environment [18] have been proposed, greatly expanding the realm of experimental activities attainable with Kilobots.…”

Section: Introductionmentioning

confidence: 99%

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Simulating Kilobots Within ARGoS: Models and Experimental Validation

Pinciroli

Talamali

Reina

et al. 2018

Lecture Notes in Computer Science

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The Kilobot is a popular platform for swarm robotics research due to its low cost and ease of manufacturing. Despite this, the effort to bootstrap the design of new behaviours and the time necessary to develop and debug new behaviours is considerable. To make this process less burdensome, high-performing and flexible simulation tools are important. In this paper, we present a plugin for the ARGoS simulator designed to simplify and accelerate experimentation with Kilobots. First, the plugin supports cross-compiling against the real robot platform, removing the need to translate algorithms across different languages. Second, it is highly configurable to match the real robot behaviour. Third, it is fast and allows running simulations with several hundreds of Kilobots in a fraction of real time. We present the design choices that drove our work and report on experiments with physical robots performed to validate simulated behaviours.

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Patching interpretable And‐Or‐Graph knowledge representation using augmented reality

Liu

Zhu

2021

Applied AI Letters

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We present a novel augmented reality (AR) interface to provide effective means to diagnose a robot's erroneous behaviors, endow it with new skills, and patch its knowledge structure represented by an And-Or-Graph (AOG). Specifically, an AOG representation of opening medicine bottles is learned from human demonstration and yields a hierarchical structure that captures the spatiotemporal compositional nature of the given task, which is highly interpretable for the users. Through a series of psychological experiments, we demonstrate that the explanations of a robotic system, inherited from and produced by the AOG, can better foster human trust compared to other forms of explanations. Moreover, by visualizing the knowledge structure and robot states, the AR interface allows human users to intuitively understand what the robot knows, supervise the robot's task planner, and interactively teach the robot with new actions. Together, users can quickly identify the reasons for failures and conveniently patch the current knowledge structure to prevent future errors. This capability demonstrates the interpretability of our knowledge representation and the new forms of interactions afforded by the proposed AR interface.augmented reality (AR), explainable artificial intelligence (XAI), robot learning | INTRODUCTIONThe ever-growing vast amount of data and rapid-increasing computing power have recently boosted a data-driven machine learning paradigm. Despite promising progress in model performance, such pure data-driven approaches, especially methods using deep neural networks, have one well-known limitation-the lack of interpretability. Various attempts, therefore, have been made to alleviate this shortcoming, such as visualizing filter responses, 1-3 developing communication protocols, [4][5][6][7] and generating text descriptions for images, 8,9 or robot behaviors. 10,11 However, these explanation mechanisms only suffice with two-dimensional (2D) interactions (ie, using computer screens) and fall short of interacting with physical robots, especially in some mission-critical settings that require supervising multiple robots. Therefore, an interpretable knowledge representation for robots and an effective explanation interface beyond 2D are needed for better situational awareness, richer spatial information, and in situ explanations during human-robot interaction. Of note, less attention has been paid, for physical robots, in introducing

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ARK: Augmented Reality for Kilobots

Cited by 59 publications

References 21 publications

ColCOS Φ: A Multiple Pheromone Communication System for Swarm Robotics and Social Insects Research

ColCOS Φ: A Multiple Pheromone Communication System for Swarm Robotics and Social Insects Research

Simulating Kilobots Within ARGoS: Models and Experimental Validation

Patching interpretable And‐Or‐Graph knowledge representation using augmented reality

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