Adaptable Platform for Interactive Swarm Robotics (APIS): A Human-Swarm Interaction Research Testbed

Dhanaraj, Neel; Maffeo, Julietta; Pereira, Guilherme A. S.; Gross, Jason N.; Gu, Yu; Hewitt, Nathan; Edmonds-Estes, Casey; Jarman, Rachel; Seo, Jeong-Woo; Gunner, Henry; Hatfield, Alexandra; Johnson, Tucker F.; Yifru, Lunet

doi:10.1109/icar46387.2019.8981628

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…In another work, Dhanaraj et al [23] propose an Adaptable Platform for Interactive Swarm robotics (APIS) to design and develop human-swarm interaction applications. The platform uses fifty low-cost robots and modular software architecture, which allows the development of swarm-level algorithms.…”

Section: Related Workmentioning

confidence: 99%

SHaSTA: An Open-Source Simulator for Human and Swarm Team Applications

Manjunatha,

KrisshnaKumar,

Distefano

et al. 2024

Preprint

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Human-swarm teaming combines the dynamic capabilities of robot swarms with the strategic cognition of humans to perform complex tasks. As robotic systems and unmanned vehicles gain enhanced autonomy, there is an imperative need for for a human-in-the-loop simulation framework to study both human action/cognition as well as swarm behaviors and their interaction within operational contexts. Such a framework is essential for developing and testing the coordination of human supervisor and robotic swarms with different level of autonomy and exploring the role of machine learning in enhancing their collective behaviors beyond basic functionalities. This paper introduces SHaSTA(Simulator for Human And Swarm Team Applications), a versatile, open-source simulation platform designed to advance research in swarm learning and human-swarm interaction. SHaSTA offers a comprehensive interface that allows both human operators and machine learning algorithms to direct and refine swarm tactics and behaviors. The platform incorporates a range of functionalities, including individual robot controls, swarm behavior primitives like formation control and path planning, and customizable interfaces for defining novel behavioral primitives. A standout feature of SHaSTA is its capability to integrate and synchronize physiological data from human operators with robotic swarm simulations. This integration provides a deeper understanding of human factors in swarm control and enhances the simulation’s realism and applicability. We demonstrate SHaSTA’s effectiveness and scalability through a case study in a search-andrescue scenario, showcasing its computational efficiency and the practical benefits of its human-swarm interaction model. Additionally, we present findings from our human subject study that illustrate how SHaSTA effectively captures and utilizes physiological features to inform and improve human-swarm interaction strategies.

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Section: Related Workmentioning

confidence: 99%

SHaSTA: An Open-Source Simulator for Human and Swarm Team Applications

Manjunatha,

KrisshnaKumar,

Distefano

et al. 2024

Preprint

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“…У наукових роботах Barcis A., Blender T., Dhanaraj N., показано, що один інтелектуальний мобільний «s-bot» не завжди може ефективно виконати завдання однієї «Swarm-bot» system, зокрема, через невеликий, як правило, радіус обмежений енергетичний ресурс, обмежену кількість операцій, які він здатний виконувати, і, нарешті, невисоку ймовірність досягнення мети в екстремальних умовах, пов'язаних із можливістю виходу з ладу одного інтелектуального мобільного «s-bot» [3][4][5].…”

Section: аналіз останніх досліджень та публікаційunclassified

Взаємодія Між «s-Bots» Однієї «swarm-Bot» System У Фізичному Неорганізованому Середовищі

Krivoulya¹,

Tokariev²,

Ilina³

et al. 2023

CNCS

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З появою принципово нових «Swarm-bot» systems, з перебудовуваною структурою та програмованою логікою, виникла необхідність урахування факторів, які взаємодіють з фізичним неорганізованим середовищем, збільшенням числа елементів, що входять до їх складу, а також, числом внутрішніх зв'язків. Особливістю нових «Swarm-bot» systems є те, що їх функції, параметри, структури та поведінка при впливі внутрішніх або зовнішніх факторів на різних часових інтервалах життєвого циклу можуть змінюватися або програмним, або апаратним способом. В даний час доведено, що вирішення складних завдань є більш ефективним тоді, коли застосовуються «Swarm-bot» systems в цілому, а не окремі елементи, що входять до їх складу, наприклад, окремі «s-bots», так як при застосуванні «Swarm-bot» systems значно збільшується радіус дії за рахунок розосередження «s-bots», що входять до складу цієї «Swarm-bot» system по всій заданій поверхні, що значно підвищує шанси покрити всю поверхню без зон пропуску (так званих сліпих зон), у яких Drones не зможуть здійснити комунікацію між собою. Ця стаття присвячена вирішенню задачі покриття заданої поверхні регулярними багатокутниками, вписаними в коло.

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“…The swarm is controlled via light patterns projected using an overhead projector (Le Goc et al, 2016). The APIS 19 (Adaptable Platform for Interactive Swarm) comprises several components: the swarm robotic platforms, the infrastructure and test environment for the swarm, and the software infrastructure and simulation (Dhanaraj et al, 2019). The focus are experiments related to human-swarm interaction.…”

Section: Terrestrialmentioning

confidence: 99%

Swarm Robotic Behaviors and Current Applications

et al. 2020

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In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

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Adaptable Platform for Interactive Swarm Robotics (APIS): A Human-Swarm Interaction Research Testbed

Cited by 10 publications

References 26 publications

SHaSTA: An Open-Source Simulator for Human and Swarm Team Applications

SHaSTA: An Open-Source Simulator for Human and Swarm Team Applications

Взаємодія Між «s-Bots» Однієї «swarm-Bot» System У Фізичному Неорганізованому Середовищі

Swarm Robotic Behaviors and Current Applications

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