Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.
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http://dx.doi.org/10.1126/scirobotics.aau9178 | 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
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.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan.
Drawing inspiration from natural herding behaviours, shepherding provides a method for swarm guidance that utilizes steering agents and can be applied in biological and robotics systems at various scales. However, while most shepherding research has relied on the precise sensing capabilities of steering agents, these assumptions do not necessarily hold in real-world tasks. To fill in the gap between practice and literature, in this study, we demonstrate that swarm shepherding can be achieved via bearing-only measurements, and explore the minimum amount of information required.
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