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Programmable self-assembly in a thousand-robot swarm
Abstract
Large-scale robotic self-assembly: When individuals swarm, they must somehow communicate to direct collective motion. Swarms of robots need to deal with outliers, such as robots that move more slowly than the rest. Rubenstein et al. created a large swarm of programmed robots that can form collaborations using only local information. The robots could communicate only with nearby members, within about three times their diameter. They were able to assemble into complex preprogrammed shapes. If the robots' formation hit snags when they bumped into one another or because of an outlier, additional algorithms guided them to rectify their collective movements. Science , this issue p. 795
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Guiding the Self-Organization of Cyber-Physical Systems
Gershenson, C. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00041 · Cited by 23
“There are many cases where self-organization has been used as an approach in engineering (Di Marzo Serugendo et al., 2004; De Wolf et al., 2005; Zambonelli and Rana, 2005; Mamei et al., 2006; Helbing et al., 2007; Dressler, 2008; Müller-Schloer et al., 2011; Rohden et al., 2012; Brambilla et al., 2013; Rubenstein et a…”
MethodA stochastic approach to shortcut bridging in programmable matter
Arroyo, M. A., Cannon, S., Daymude, J. J., et al. (2018) · Natural Computing · doi:10.1007/s11047-018-9714-x · Cited by 21
“2012), shape formation (Flocchini et al. 2008; Rubenstein et al. 2014), and imitating the collective behavior of natural systems (Chazelle 2009); however, the individual robots typically have more powerful communication and processing capabilities than those we consider.”
BackgroundFormation Control of UAVs and Mobile Robots Using Self-organized Communication Topologies
Zhu, W., Allwright, M., Heinrich, M. K., Oğuz, S., Christensen, A. L., & Dorigo, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60376-2_25 · Cited by 25
Torch: Strategy evolution in swarm robots using heterogeneous–homogeneous coevolution method
Wu, M., Zhu, X., Ma, L., Wang, J., Bao, W., Li, W., & Fan, Z. (2021) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100239 · Cited by 21
Phototactic micromotor assemblies in dynamic line formations for wide-range micromanipulations
Guo, X., Wang, Y., Mou, F., Xie, Q., Su, S., Chen, C., & Guan, J. (2022) · Journal of Materials Chemistry C · doi:10.1039/d1tc06078c · Cited by 20
Motility-induced phase separation and frustration in active matter swarmalators
Adorjáni, B., Libál, A., Reichhardt, C. M., et al. (2024) · Physical review. E · doi:10.1103/physreve.109.024607 · Cited by 20
“Active matter systems contain self-motile particles and can be found in soft matter [1–3], biological [4–8], social [9, 10], active topological [11, 12], and robotic [13–15] contexts.”
BackgroundProgramming tunable active dynamics in a self-propelled robot
Paramanick, S., Pal, A., Soni, H., et al. (2024) · The European Physical Journal E · doi:10.1140/epje/s10189-024-00430-x · Cited by 20
“While there are many examples in the literature where robots perform programmable complex self-assembly [33] as well as spontaneous collective behavior [34–37] they rely on vibration motors for locomotion [33–35].”
Xu, Y., Wang, H., Liu, X., et al. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3461809 · Cited by 20
“S YNCHRONIZATION, being one of the most interesting cooperative behaviors in natural and man-made networks, has several applications in many areas such as power transmission [1], robot cooperative control [2], multiple vehicles [3], and so on [4], [5], [6].”
Deep Q-Learning for Dry Stacking Irregular Objects
Liu, Y., Shamsi, S. M., Fang, L., Chen, C., & Napp, N. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593619 · Cited by 22
“Approaches where multiple robots build structures that are large compared to themselves are considered various settings, that include decentralized approximation of large 2D structures [5], construction of 3D structures with robots that can navigate partial structures [6], and flying robots [7] that use building eleme…”
Nimmo, R. (2021) · Environment and Planning E Nature and Space · doi:10.1177/2514848620987368 · Cited by 21
“robot together with a ‘smart algorithm’ to successfully assemble itself into a series of humandirected shapes and letters, even self-correcting errors in the process, in a demonstration of the potential of collective artificial intelligence (Rubenstein et al., 2014).”
BackgroundMethodCoverage on demand: A simple motion control algorithm for autonomous robotic sensor networks
Zhao, H., Mao, L., & Wei, J. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.02.004 · Cited by 20
Pushing Lines Helps: Efficient Universal Centralised Transformations for Programmable Matter
Almethen, A., Michail, O., & Potapov, I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34405-4_3 · Cited by 20
The Morphospace of ConsciousnessPreprint
Arsiwalla, X. D., Moulin-Frier, C., Herreros, I., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2017) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/emhnp · Cited by 19
“This bears some resemblance to the notion of collective intelligence, which is a widely studied phenomenon in complex systems ranging from ant colonies [30], to a swarm of robots (the Kilobot in [79] and the CoRobot in [42]), to social networks [38].”
BackgroundInterlocking Block Assembly With Robots
Zhang, Y., Koga, Y., & Balkcom, D. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3069742 · Cited by 19
“Kilobot, a swarm of 1024 mobile robots, was introduced by Rubenstein et al. [41], along with algorithms for planning mechanisms allowing kilobots to form 2-”
MethodChemistry-Inspired Pattern Formation With Robotic Swarms
Rezeck, P. A. F., & Chaimowicz, L. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3190638 · Cited by 19
“ment of defining positions describing the patterns consists of using seed robots in which some robots do not move and act as a reference to the others helping the swarm to create complex global patterns [27], [28].”
Voting-Based Scheme for Leader Election in Lead-Follow UAV Swarm with Constrained Communication
Zuo, Y., Yao, W., Chang, Q., Zhu, X., Gui, J., & Qin, J. (2022) · Electronics · doi:10.3390/electronics11142143 · Cited by 19
Multi-agent deployment in 3-D via reaction–diffusion system with radially-varying reaction
Zhang, J., Vázquez, R., Qi, J., et al. (2024) · Automatica · doi:10.1016/j.automatica.2023.111491 · Cited by 19
“More recent work have dramatically expanded the scale of these multi-agent systems (Bandyopadhyay et al., 2017; Chung et al., 2018; Rubenstein et al., 2014).”
BackgroundClassifying swarm behavior via compressive subspace learning
Berger, M., Seversky, L. M., & Brown, D. S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487744 · Cited by 19
Reichhardt, C. J. O., Reichhardt, C. M., Reichhardt, C. M., et al. (2016) · Nature Physics · doi:10.1038/nphys3929 · Cited by 19
Formation tracking control for multi-agent systems: A wave-equation based approach
Tang, S., Qi, J., & Zhang, J. (2017) · International Journal of Control Automation and Systems · doi:10.1007/s12555-016-0562-0 · Cited by 19
“Recently, the coordinate control of multi-agent system with thousands of agents has attracted great attention [6, 7].”
Background
Metadata
- Authors
- Rubenstein, M., Cornejo, A., & Nagpal, R.
- Affiliations
- Harvard University (US)
- Corresponding author
- Rubenstein, Michael
- Venue
- Science
- Published
- 2014-08-15
- Volume
- 345
- Issue
- 6198
- Pages
- 795-799
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2030775133
- Semantic Scholar
- c5ab840850ab57f2f14b67a5485fa3fd727b33b8
- Cited by
- 1,308
- References
- 22
- Citations by year
- 2026: 115 · 2025: 129 · 2024: 110 · 2023: 122 · 2022: 118 · 2021: 112 · 2020: 105 · 2019: 135 · 2018: 117 · 2017: 94 · 2016: 73 · 2015: 66 · 2014: 11
- FWCI
- 98.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Micro and Nano Robotics
- Keywords
- programmable self-assembly, swarm robotics, shape formation, decentralized systems, autonomous robots, local interactions, artificial swarms, two-dimensional shapes
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