Advances in artificial intelligence (AI) and robotics are accelerating progress in swarm systems. Large and bulky autonomous systems are being replaced with many, smaller, cheaper, distributed, decentralized and collectively smarter systems. However, developing these swarm intelligence systems comes with multiple challenges, including technological challenges to engineer smaller and smarter machines, interaction challenges to design novel interfaces and modalities for communication and sociotechnical challenges related to trustworthiness, ethics and legalities. Swarm systems present philosophical and mathematical dilemmas that are worthy of deeper scientific inquiry. This introduction contextualizes the topic in the contemporary literature and shows the current research directions and evolution of swarm systems using 11 carefully selected papers in this special theme. We conclude by discussing the way forward for swarm systems.This article is part of the theme issue 'The road forward with swarm systems'.
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http://dx.doi.org/10.1098/rsta.2024.0135 | DOI Listing |
Sci Rep
January 2025
School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Air Force Research Laboratory 711th Human Performance Wing, Wright-Patterson AFB, OH, USA.
We adopt a tripart approach in describing the human-centred challenges with human-swarm interaction. First, the results of large-N laboratory studies will be discussed which found evidence of trust biases (e.g.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Brigham Young University, Provo, UT, USA.
There are powerful tools for modelling swarms that have strong spatial structures like flocks of birds, schools of fish and formations of drones, but relatively little work on developing formalisms for other swarm structures like hub-based colonies doing foraging, maintaining a nest or selecting a new nest site. We present a method for finding low-dimensional representations of swarm state for simulated homogeneous hub-based colonies solving the best-of-N problem. The embeddings are obtained from latent representations of convolution-based graph neural network architectures and have the property that swarm states which have similar performance have very similar embeddings.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Bristol Robotics Laboratory, School of Engineering Mathematics and Technology, University of Bristol, Bristol BS8 1TW, UK.
In this paper, we address the question: what practices would be required for the responsible design and operation of real-world swarm robotic systems? We argue that swarm robotic systems must be developed and operated within a framework of ethical governance. We will also explore the human factors surrounding the operation and management of swarm systems, advancing the view that human factors are no less important to swarm robots than social robots. Ethical governance must be anticipatory, and a powerful method for practical anticipatory governance is ethical risk assessment (ERA).
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Binghamton Center of Complex Systems, Binghamton University, State University of New York, Binghamton, NY 13902, USA.
Artificial swarm systems have been extensively studied and used in computer science, robotics, engineering and other technological fields, primarily as a platform for implementing robust distributed systems to achieve pre-defined objectives. However, such swarm systems, especially heterogeneous ones, can also be utilized as an ideal platform for creating open-ended evolutionary dynamics that do not converge toward pre-defined goals but keep exploring diverse possibilities and generating novel outputs indefinitely. In this article, we review Swarm Chemistry and its variants as concrete sample cases to illustrate beneficial characteristics of heterogeneous swarm systems, including the cardinality leap of design spaces, multi-scale structures/behaviours and their diversity, and robust self-organization, self-repair and ecological interactions of emergent patterns, all of which serve as the driving forces for open-ended evolutionary processes.
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