The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
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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Cites
Whitesides, G. M., & Grzybowski, B. A. (2002) · Science · doi:10.1126/science.1070821 · Cited by 7,550
Folding DNA to create nanoscale shapes and patterns
Rothemund, P. W. K. (2006) · Nature · doi:10.1038/nature04586 · Cited by 7,474
Range-free localization schemes for large scale sensor networks
He, T., Huang, C., Blum, B. M., et al. (2003) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/938985.938995 · Cited by 2,658
The Rotary Motor of Bacterial Flagella
Berg, H. C. (2003) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.72.121801.161737 · Cited by 1,437
Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses
Wurman, P. R., D'Andrea, R., & Mountz, M. (2007) · The AI Magazine · doi:10.1609/aimag.v29i1.2082 · Cited by 1,151
Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network
Nagpal, R., Shrobe, H. E., & Bachrach, J. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36978-3_22 · Cited by 759
Self-citationCoordinating hundreds of cooperative, autonomous vehicles in warehouses
Wurman, P. R., & Mountz, M. (2008) · Cited by 566
The making of a fly; The genetics of animal design
(1992) · International Journal of Biochemistry · doi:10.1016/0020-711x(92)90084-e · Cited by 407
Autonomous Self-Assembly in Swarm-Bots
Groβ, R., Michael Bonani, C., Francesco Mondada, C., & Dorigo, M. (2006) · IEEE Transactions on Robotics · doi:10.1109/tro.2006.882919 · Cited by 351
Goldstein, S. C., Campbell, J. D., & Mowry, T. C. (2005) · Computer · doi:10.1109/mc.2005.198 · Cited by 327
Research works printed but not linked: 8
- S. Camazine et al . Self-Organization in Biological Systems (Princeton Univ. Press Princeton NJ 2003).
- M. Kirschner J. Gerhart The Plausibility of Life: Resolving Darwin’s Dilemma (Yale Univ. Press New Haven CT 2005).
- E. William G Mermoud A. Martinoli in Proceedings of the 2010 International Conference on Robotics and Automation (Anchorage AK May 2010) pp. 1438–1445.
- M. Rubenstein C. Ahler R. Nagpal in Proceedings of the 2012 IEEE International Conference on Robotics and Automation (St. Paul MN May 2012) pp. 3293–3298.
- P. Vartholomeos E. Papadopoulos in Proceedings of the 2006 IEEE International Conference on Robotics and Automation (Orlando FL May 2006) pp. 649–654.
- D. Moore J. Leonard D. Rus S. Teller in The 2nd International Conference on Embedded Networked Sensor Systems (Baltimore November 2004) pp. 50–61.
- J. Werfel Y. Bar-Yam D. Rus R. Nagpal in Proceedings of the 2006 IEEE International Conference on Robotics and Automation (Orlando FL May 2006) pp. 2787–2794.
- M. De Rosa S. Goldstein P. Lee J. Campbell P. Pillai in Proceedings of the 2006 IEEE International Conference on Robotics and Automation (Orlando FL May 2006) pp. 1462–1468.
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Qu, Y., Xin, B., Wang, Q., et al. (2022) · Scientia Sinica Technologica · doi:10.1360/sst-2021-0349
Valentini, G. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-53609-5_1
(2014) · Nature · doi:10.1038/512235e
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Yao, Y., Kathleen Marchal, D., & de Peer, Y. V. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-8060-7.ch046
Wortham, R. H., & Bryson, J. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780199674923.003.0033
Tang, S., & Kumar, V. (2018) · Springer eBooks · doi:10.1007/978-3-642-41610-1_21-1
PDE-based Multi-agent Deployment in 3-D Space via Bilateral Boundary control
Chen, Y., Qi, J., & Liu, Y. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727901
“For example, the cluster control of more than 100 small robots [1]–[3] and the control of small satellites [4].”
BackgroundAranda, M., Nicolás, G. L., & Sagüés, C. (2017) · Advances in industrial control · doi:10.1007/978-3-319-57828-6_1
Veitch, C. (2019) · doi:10.24124/2019/59010
Binysh, J., & Souslov, A. (2022) · Nature Physics · doi:10.1038/s41567-022-01735-4
Uma proposta para impulsionar as pesquisas em governança de algoritmos de IA e dadosPreprint
Braga, J., Regateiro, F. S., Stiubiener, I., & Braga, J. C. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/xcpsd
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Kazeruni, N. M. B., Tsitkov, S., & Hess, H. (2019) · Journal of Visualized Experiments · doi:10.3791/59068-v
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Wang, J., & Rubenstein, M. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981377
“Additionally, a prototype robot is used to demonstrate its ability to move in a straight line as well as follow given paths.”
MethodSelf-citationHasenfuss, H. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-22015-9_3
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Ilday, S., & Ilday, F. Ö. (2023) · Springer series in optical sciences/SPringer series in optical sciences · doi:10.1007/978-3-031-14752-4_4
Field-driven cluster formation in two-dimensional colloidal binary mixtures
Qian, D., & de la Cruz, M. O. (2023) · Physical review. E · doi:10.1103/physreve.107.044605
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Yu, J., Wang, B., Du, X., et al. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-822125-9.00008-7
DNA Nanotechnology Research in Japan
Murata, S. (2023) · Natural computing series · doi:10.1007/978-981-19-9891-1_4
Distributed Large-Scale Swarm Control in Obstacle Environment Based on Random Finite Set Theory
Sun, J., Lin, D., He, S., Hussain, I., & Seneviratne, L. D. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3294481
“Forinstance,anassemble experimentof100Kilobots(atypeoflow-costrobot)takes 140stoconvergetothedesiredpositions[4],whileanexper-imentwith1024Kilobotstakes12h[8].”
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Wang, L., Yu, Y., Zhang, D., & Yang, H. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240675 · Cited by 1
“Either explicit/implicit communication between robots or the global knowledge of robots and/or goals is generally required in the study of shape formation problems [5, 21].”
BackgroundFrom Robot Self-Localization to Global-Localization: An RSSI Based Approach.
Lentzas, A., & Vrakas, D. (2023) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.391.4
“The benefits of localizing the swarm in a common coordinate system are presented in [18].”
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Zhang, J., Li, H., Du, H., Liang, Y., Song, W., & Li, T. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6498-7_37
Pattern Transformation Rate Control of Magnetic Microswarms
Zhang, L., Yang, S., Wang, Q., & Jin, D. (2023) · Springer tracts in electrical and electronics engineering · doi:10.1007/978-981-99-3036-4_6
Introduction to Micro/Nanorobot Swarms
Zhang, L., Yang, S., Wang, Q., & Jin, D. (2023) · Springer tracts in electrical and electronics engineering · doi:10.1007/978-981-99-3036-4_1
Heterogeneous Colloidal Microswarm with Multifunction
Zhang, L., Yang, S., Wang, Q., & Jin, D. (2023) · Springer tracts in electrical and electronics engineering · doi:10.1007/978-981-99-3036-4_4
Ant Bridge-Mimicked Reconfigurable Microswarm for Electronic Application
Zhang, L., Yang, S., Wang, Q., & Jin, D. (2023) · Springer tracts in electrical and electronics engineering · doi:10.1007/978-981-99-3036-4_12
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Zhang, L., Yang, S., Wang, Q., et al. (2023) · Springer tracts in electrical and electronics engineering · doi:10.1007/978-981-99-3036-4_3
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Mina, T., Jo, W., Kannan, S. S., et al. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341782
“The most successful implementation of large-scale multiagent shape formation to date following local interactions have been achieved by the Kilobot platform [25]; a programmable self-assembly of complex two-dimensional shapes with a thousand Kilobot swarm was successfully demonstrated in [26].”
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Zhang, Z., Wang, T., & Peng, X. (2023) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl55364.2023.10364483
“Michael et al. [3] proposed three primitive collective behaviors, including edge-following, gradient formation and with pxg@nwpu.edu.cn . localization, to realize programmable self-assembly of complex two-dimensional shapes with 1,024 kilobots [4].”
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Zheng, Y., Wang, Q., Zou, J., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4719941
Imahayashi, W., Tsunoda, Y., & Ogura, M. (2024) · Advanced Robotics · doi:10.1080/01691864.2024.2321598
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lu, J., Wen, L., Li, L., & Sun, G. (2024) · Scientia Sinica Technologica · doi:10.1360/sst-2023-0410
Kedwan, F. (2024) · European Journal of Applied Science Engineering and Technology · doi:10.59324/ejaset.2024.2(2).14
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Nishikawa, K., Origane, Y., Etchu, H., & Kurabayashi, D. (2024) · Symmetry · doi:10.3390/sym16050630
Maegochi, S. (2024) · Springer theses · doi:10.1007/978-981-97-2987-6_1
Gálvez, A., Moustafa, N., Siang, C. V., & Iglesias, A. (2024) · Cognitive Models and Artificial Intelligence Conference (AICCONF) · doi:10.1145/3660853.3660880
“Notably, a swarm comprising one thousand kilobots has been documented [39].”
BackgroundReinken, H. (2024) · Springer theses · doi:10.1007/978-3-031-67636-9_1
Reinken, H. (2024) · Springer theses · doi:10.1007/978-3-031-67636-9_2
Kroc, J. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1007907
An efficient electromagnetic scattering character restoration solution for UAV swarm targets
Liu, J., Xu, Q. Y., & Su, M. (2024) · Remote Sensing Letters · doi:10.1080/2150704x.2024.2439079
“Swarm formulations of UAVs offer more application options (Rubenstein, Cornejo, and Nagpal 2014; Keller et al. 2017).”
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Hu, B.-B., & Zhang, H. (2024) · Cognitive intelligence and robotics · doi:10.1007/978-981-97-9214-6_1
Shape formation of swarm robots based on parallel strategy
Jia, Y., Zhao, J., & Guo, Y. F. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adb377
Li, C., Yang, Y., & Chen, X. (2025) · Symmetry · doi:10.3390/sym17020282
Sharma, S. K., Pasricha, R., Zhang, Q., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5141296
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Li, W., Liu, T., Liu, Q., et al. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2189-7_8
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Wang, Y., Pan, M., Qin, X., Hao, L., & Yang, Y. (2024) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio64047.2024.10907524
3D Robotic Control of Micro-Scale Optical Swarms at an Interface
Carlisle, N., Nock, V., Williams, M. A. K., Whitby, C. P., Chen, J. L. ‐., & Avci, E. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3568304
“Optical force-induced assembly is a promising approach for developing a unique micromanufacturing approach inspired by the Kilobot thousand robot swarm [18], [19], where assemblies are produced, transported to, and bonded to a base assembly to form a larger complex structure.”
Environmentally-Driven Morphogenesis of Reconfigurable Floating Structures using Markov Models
Obayashi, N., Meyer, G., Junge, K., & Hughes, J. (2025) · IEEE International Conference on Soft Robotics (RoboSoft) · doi:10.1109/robosoft63089.2025.11020845 · Cited by 1
“However, integrating numerous intelligent units within such systems presents challenges in terms of hardware complexity and programming [9], [10].”
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Mastej, B. (2025) · International Journal of Electronics and Telecommunications · doi:10.24425/ijet.2025.153606
“Example: creating a local coordinate system [13]. c) Decision-making: - a class of behaviors relating to broadly understood decision-making within the swarm: • Consensus achievement - making a joint decision for all members of a swarm or its subgroup.”
Fluidic robot swarm control for obstacle-unaware navigation
Eguchi, M., Nishimura, M., Yoshida, S., et al. (2025) · Advanced Robotics · doi:10.1080/01691864.2025.2517214
“Real-world applications leveraging small robot swarms’ robustness, flexibility, and scalability have been extensively explored [22,23].”
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Wang, J., Chen, H., & Liu, L. (2025) · 中国科学基金 · doi:10.3724/bnsfc-2025.02.27.0002
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Meng, F., Ma, W., Cheng, R., et al. (2025) · Chinese Physics B · doi:10.1088/1674-1056/adf4a7
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Loges, M., & Weiss, T. (2025) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3747868
“For example, Mayet and colleagues focused on emulating ant pheromone trail behavior [Mayet et al. 2010], and Rubenstien et al. proposed to use robots for mimicking ant self-assembly behaviors [Rubenstein et al. 2014].”
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Connor, M., Michail, O., & Skretas, G. (2025) · Theoretical Computer Science · doi:10.1016/j.tcs.2025.115521
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Hauert, S. (2014) · doi:10.64628/ab.jwdwjg76k
Xie, Y., & Fumiya Iida, A. (2025) · Evolution of Nervous Systems · doi:10.1016/b978-0-443-27380-3.00033-6
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Dokuyucu, H. İ., & Özmen, N. G. (2025) · Konya Journal of Engineering Sciences · doi:10.36306/konjes.1660279
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Hou, J., Qiu, Y., Yang, Y., & Liu, L. (2025) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber67662.2025.11168353
“The pursuit–evasion problem has recently become a central issue in many application area of robotics [9], including area coverage [10], perimeter defense [11], and collecting livestock [12].”
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Liu, J., Xu, Q. Y., Su, M., et al. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3614657
“In the military, UAV swarms could conduct various missions like field surveillance, electronic interference, suicide attacks, etc., [5], [6], [7], [8].”
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Xin, L., Zuo, K., Shaorong, X., & Peng, J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114026
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Dizenhaus, M., Simone, F. D., & Patterson, G. A. (2026) · Physical review. E · doi:10.1103/rntx-zg23
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Clinton, J., Yi, S., & Temel, Z. (2025) · Springer proceedings in advanced robotics · doi:10.1007/978-3-032-04584-3_9
SUEOKA, Y., Kato, Y., Yoshida, T., Osuka, K., Yajima, R., Chikushi, S., Nagatani, K., & Asama, H. (2025) · Springer proceedings in advanced robotics · doi:10.1007/978-3-032-04584-3_25 · Cited by 1
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Jones, S., & Hauert, S. (2025) · Springer proceedings in advanced robotics · doi:10.1007/978-3-032-04584-3_36
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Lange, J., Schweizer, P., Al-Falouji, G., et al. (2025) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) · doi:10.1109/acsos-c66519.2025.00036
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Ishikane, M., Li, Y., Minami, Y., et al. (2025) · Artificial Life and Robotics · doi:10.1007/s10015-025-01074-6
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Ding, J., Jakkula, R., Xiao, T., et al. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246141
“These systems demonstrate significant potential in applications requiring adaptability, scalability, and robustness, such as search-and-rescue [4], planetary exploration [5], and autonomous manufacturing and inspection [6], where the ability to reconfigure and recover from failures is critical for…”
Contreras-Monsalvo, J. F., Dossetti, V., & Soto-Cruz, B. S. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae2766
“…for studies in swarm robotics with the deployment of large numbers of individuals, typically addressing self-assembly, self-organization and emergent collective behavior phenomena in laboratory conditions, where external sources of light (and noise) are controlled [15, 17, 40, 52, 51, 53, 54].”
Decentralized Fluid-Inspired Swarm Shape Formation for Dynamic Targets
Yang, J., Zhang, J., & Zhang, C. (2025) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1109/aiahpc66801.2025.11290188
“Existing approaches broadly split into goal-assignment methods, which allocate robots to specific targets and achieve high spatial accuracy [3],[4], and self-organizing strategies inspired by biological collectives, which offer adaptability but less control over spatial fidelity [5],[6].”
Hashmi, A. S., & Majid, I. (2025) · Journal of Robotics Research · doi:10.64820/aepjrr.21.14.19.62025
Frolov, O., Safin, R. N., Tsoy Tatyana, G., et al. (2025) · Učenye zapiski Kazanskogo gosudarstvennogo universiteta. Seriâ Fiziko-matematičeskie nauki/Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki · doi:10.26907/2541-7746.2025.4.786-805
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Zhu, S., & Xi, Z. (2025) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus66297.2025.11294588
“[9] report how to produce a large-scale robotic self-assembly system, which demonstrates by thousand-robot swarm to achieve highly complex collective behaviors.”
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Bogue, R. (2026) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-12-2025-0478
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Singh, S., Ferlazzo, J., Wang, S., Boateng, S. N., Martinez, M. O., Blumenschein, L. H., & Temel, Z. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3655268
Aguirre, G., Bingöl, S. A., Hamann, H., et al. (2025) · doi:10.1109/mrs66243.2025.11357254
“2 It simulates a swarm of robots with capabilities comparable to Kilobots [31].”
Cellular morphology and density control kinetic energy spectra in bacterial monolayer swarms
Chen, X., Yuan, M., Zhang, H., et al. (2026) · Soft Matter · doi:10.1039/d5sm01075f
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Le, N. H., Erickson, P., Zhang, Y. L., et al. (2025) · International Conference on Computational Intelligence and Intelligent Systems (CIIS) · doi:10.1145/3787256.3787274
“Enabling lifelike systems to be shaped directly by natural language could radically expand our ability to interact with them, unlocking applications in artificial morphogenesis [20], collective robotics [31], and programmable synthetic intelligence [33].”
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Pignedoli, A., Majumdar, A., & Everschor‐Sitte, K. (2026) · Physical Review Research · doi:10.1103/k8wj-xsl7
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Sujatha, E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6173748
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Giardina, F., Prasath, S. G., & Mahadevan, L. (2026) · PRX Life · doi:10.1103/cx3h-bwhc
(2026) · Elsevier eBooks · doi:10.1016/b978-0-44-327410-7.00040-5
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Bienau, A., Boche, H., Deppe, C., Fitzek, F. H. P., Hofmann, P., Kellerer, W., Labidi, W., Richter, A., Simmel, F. C., Wietfeld, A., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-327410-7.00036-3 · Cited by 1
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Liu, L., Werfel, J., Toschi, F., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2519032123
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Ellis, N., Kelly, J., Young, I., Caravaggio, A., Manfredini, F., & Giannaccini, M. E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6249640
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Liu, K., Huang, T., Li, A., Lv, P., Qin, T., Chen, W., Wei, H., & Duan, H. (2026) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500178 · Cited by 1
Singh, R. K. (2026) · i-manager s Journal on Augmented & Virtual Reality · doi:10.26634/javr.3.2.22908
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Khan, I. I., Parvaz, M., & Kolankeh, A. K. (2025) · doi:10.1109/icamac67779.2025.11398586
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InfluentialLiu, J., Si, S., Xu, Q. Y., et al. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3670875
“Swarm formulations of UAVs are new derivations of their application solutions for conducting diverse mission types like surveillance, suicide attacks, electric interferences, etc., [5], [6], [7], [8].”
Higuchi, K., Goto, M., Kadomoto, J., & Irie, H. (2026) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/3731459.3779329
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“The Kilobot (Rubenstein et al., 2014a ) is a widely used platform in swarm robotics research, thanks to its simplicity, small size and low cost that enable experiments with dozens or hundreds of robots at the same time (Rubenstein et al., 2014b ; Valentini et al., 2016 ; Pluhacek et al., 2023 ;…”
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Raghavendra, T. S., Shelke, Y. G., van der Ham, S., Anpuj Nair, S., & Vutukuri, H. R. (2026) · Communications Physics · doi:10.1038/s42005-026-02596-6
“Beyond single-particledynamics,distinctpropulsionmodesin fl uence clustering behavior, leading to differences in cluster growth between ABPs and particles undergoing circular motion.”
Decentralized Emergent Behavior Design in Robotic Swarms Using Gibbs Random Fields
Rezeck, P. A. F., & Chaimowicz, L. (2026) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-026-02382-0
“Other decentralized strategies leverage local-only information to form patterns without central control, offering scalability and resilience, and avoid dependencies on predefined goal assignments or extensive communication networks [49–51].”
Diatom‐Inspired 1D Immobile Robots Capable of 2D Collective Mobility
Hu, T., Zhang, Y., Qiao, C., et al. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.202501506
Duclos, G. (2015) · theses.fr (ABES) · doi:10.70675/5e54bdbdz084az479bza866z47813b1955b6
Material‐Based Intelligence: Autonomous Adaptation and Embodied Computation in Physical Substrates
Baulin, V. A., Füchslin, R. M., Giacometti, A., Häuser, H., & Werner, M. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.202501450 · Cited by 1
“Metrics could include integrated information measures [147], the complexity of sensorimotor loops as quantified via information flow between distributed parameters and actuation patterns [105, 148], or the entropy of accessible states or behavioral outputs under varied and novel stimuli [111].”
Bernard, A. (2016) · theses.fr (ABES) · doi:10.70675/e19f6de2zd04fz4ac8zb527z2176ebcbfb7d
“Rubenstein and colleagues (Rubenstein et al. 2014) used a swarm of a thousand individuals capable of selfassembly.”
BackgroundMethodHow Minimalist Bio-inspired Robots Leverage Embodiment for Locomotion in Challenging Environments
Chikere, N., & Ozkan-Aydin, Y. (2026) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1343/1/012013
“Swarm platforms such as Kilobots [114] and smarticles [118] further illustrate how minimal individual capabilities can scale to coordinated group behavior.”
Influential
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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