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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
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
Flagged first, then by citations
1 of the 22 references couldn’t be matched to a known paper.
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.
Cited by
Sorted by citations
Items 1,001–1,100 of 1,262
Formation Control Considering Well-organized Crossing of Robot Swarms
Kobayashi, Y., Yoshimoto, M., Kon, K., et al. (2017) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.35.78 · Cited by 1
Building Fractals with a Robot Swarm
Yu, Z., & Goldman, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61833-3_20 · Cited by 1
Shape Self-Assembly for a Swarm of Agents
Zhu, X., Zhao, J., Ji, J., et al. (2019) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2019.8743324 · Cited by 1
“In our work, we use the gradient communication in [19] and [20] to form the communication network, which propagates messages among the agents and helps agents select their respective local leaders.”
BackgroundMethodInfluentialImproved Artificial Moment Method for Path Planning of Swarm Robots
Ma, F., Xu, W.-B., Ma, L., et al. (2019) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8832929 · Cited by 1
Communication of Mobile Devices with Bioinspired Algorithms
Hernández-Huerta, E., Neri-Muñoz, M., Aparicio-Lozada, J. F., et al. (2020) · International Journal of Combinatorial Optimization Problems and Informatics. · doi:10.61467/2007.1558.2020.v11i3.174 · Cited by 1
“Tsai & Lin (2011) [31] study the behaviour of the fish swarm algorithm with respect to optimization during the process.”
BackgroundSocial Behavior, Cooperation, and Kinship
Breed, M. D., & Moore, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801532-2.00013-1 · Cited by 1
Control of self-organization: From equilibrium to non-equilibrium
Shi, Y., & Zhang, T. H. (2020) · Acta Physica Sinica · doi:10.7498/aps.69.20200161 · Cited by 1
Nogami, T., & Wang, Z. (2021) · IEEE International Conference on Intelligence and Safety for Robotics (ISR) · doi:10.1109/isr50024.2021.9419541 · Cited by 1
Self-Organization of Tissues Through Biochemical and Mechanical Signals
Misailidis, G., Ferenc, J., & Tsiairis, C. D. (2021) · Nečas center series · doi:10.1007/978-3-030-88084-2_2 · Cited by 1
Tan, Y., RenLong, C. H. E. N., & Zhai, T. (2022) · Scientia Sinica Technologica · doi:10.1360/sst-2021-0376 · Cited by 1
Localization in Unprecedentedly Crowded Airspace for UAVs and SUAVs
Khelifi, M., & Butun, I. (2022) · IEEE Access · doi:10.1109/access.2022.3181377 · Cited by 1
Connor, M., & Michail, O. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22050-0_5 · Cited by 1
Auction-based solution for the ordering problem in robotic self-assembly
Cabral, K., Silveira, J., Jardine, P. T., et al. (2023) · 2020 IEEE International Systems Conference (SysCon) · doi:10.1109/syscon53073.2023.10131207 · Cited by 1
“Commonly, autonomous construction uses more complex robots (commonly) embedded with weight carrying capabilities [2], [5] whilst self-assembly tasks consider simpler and homogeneous robots as the structure parts [4], [6].”
BackgroundMethodInfluentialGrasso, C., & Bongard, J. (2023) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3583131.3590469 · Cited by 1
“Understanding the mechanisms by which collective systems communicate information to achieve such global coordination could provide key insights for self-assembling robots [16, 25, 29], shapechanging robots [2, 15, 28, 32], and regenerative medicine systems [18, 20, 22].”
BackgroundFu, Y., Wu, Z., Zhan, S., et al. (2023) · Micromachines · doi:10.3390/mi14081503 · Cited by 1
“…et al. demonstrate a thousand-robot swarm (dubbed kilobots) capable of large-scale, flexible self-assembly of two-dimensional shapes entirely through programmable local interactions and local sensing [48]. fields ranging from farming and food management, to military defense and space systems [47].”
BackgroundDistributed Situation Awareness Consensus Optimization of Unmanned Group Systems
Qiu, H., Hai, X., Sun, C., et al. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240099 · Cited by 1
Adaptive Formation Tracking of Swarm Jumping Robots Using Multiagent Deep Reinforcement Learning
Zhou, Q., Li, G., Tang, R., Xu, Y., & Shi, Q. (2023) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus58632.2023.10318255 · Cited by 1
“The Harvard University team developed the Kilobot robot, which can form a large group of microrobot systems and achieve division of labor and cooperation without the guidance of a central intelligent system [10][11].”
MethodGoto, T., Kadomoto, J., Irie, H., et al. (2024) · doi:10.1145/3623509.3635267 · Cited by 1
“Examples of systems using this approach include [17, 18].”
MethodAutomated Multi-Robot Assembly of Compliance Optimized Structures
Fu, Y., Gloyd, J. T., & Kostitsyna, I. (2024) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero58975.2024.10521435 · Cited by 1
“Realizing such complex emergent behavior, however, is a challenging algorithmic task [5–7].”
BackgroundEfficiently reconfiguring a connected swarm of labeled robots
Fekete, S. P., Krämer, P., Rieck, C., et al. (2024) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-024-09668-3 · Cited by 1
“Lemma 28.”
Resilient swarm behaviors via online evolution and behavior fusion
Neupane, A., & Goodrich, M. A. (2024) · Swarm Intelligence · doi:10.1007/s11721-024-00243-w · Cited by 1
“…of ant, bee, and termite colonies to solve complicated decision processes with partial information has motivated researchers to mimic their behaviors in artificial agents and robots (Reid et al., 2015; Mlot et al., 2011; Noirot & Darlington, 2000; Rubenstein et al., 2014; Cheng et al., 2005).”
Power in numbers: Primitive algorithm for swarm robot navigation in unknown environments
Tsunoda, Y., Otsuka, S., Ito, K., et al. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2026.105329 · Cited by 1
“However, this also introduces challenges such as higher communication loads, synchronization issues, and increased coordination complexity [11], [15], [16].”
BackgroundCyber-Physical Systems for Enhancement in Security While Using Cyber
Bhatia, A., Kumar, A., & Sood, D. (2024) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/979-8-3693-7883-0.ch008 · Cited by 1
Nonequilibrium Self‐Assembly of Microtubules Through Stepwise Sequential Interactions of DNA
Keya, J. J., Akter, M., Yamasaki, Y., Kageyama, Y., Sada, K., Kuzuya, A., & Kakugo, A. (2024) · Small · doi:10.1002/smll.202408364 · Cited by 1
Kato, Y., Yoshida, T., SUEOKA, Y., Osuka, K., Yajima, R., Nagatani, K., & Asama, H. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802571 · Cited by 2
“Although prior works have attempted to engender emergent behaviors through the interaction of the robots with each other and with the environment [5], [19]–[21], they have not yet achieved a system that can perform adaptive cooperative behavior in response to unexpected situations.”
Tiwari, I., Nagappan, N., Harrison, J. S., & Bhamla, M. S. (2025) · Integrative and Comparative Biology · doi:10.1093/icb/icaf110 · Cited by 1
Loopy Movements: Emergence of Rotation in a Multicellular Robot
Smith, T. A., & Gu, Y. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128053 · Cited by 1
“Swarm robotics has demonstrated how decentralized co-ordination among agents using simple local interactions can lead to emergent bulk motion [22], [23].”
Outlook: Can Swarms Be Designed?
Bouffanais, R. (2015) · SpringerBriefs in complexity · doi:10.1007/978-981-287-751-2_7 · Cited by 1
Fu, X., Chen, W., Liu, Z., et al. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-48036-7_43 · Cited by 1
Simulation toolset for localization and control of swarming vehicles using random finite set theory
Weirens, V. A., Hisamoto, C. S., & Sheikh, S. I. (2018) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2018.8373518 · Cited by 1
“Using homogenous agents in a swarm allows for large-scale, straightforward manufacturing and development [2].”
BackgroundAn RSS-based method for path navigation in miniature robotic swarms
El-Howayek, G., Morrison, A., & Khorbotly, S. (2019) · Journal of Micro-Bio Robotics · doi:10.1007/s12213-019-00111-5 · Cited by 1
Mina, T., & Min, B. (2018) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2018.8665343 · Cited by 1
“Programmable self-assembly of multi-robots was achieved by [15] using Kilobots [16] following the boundary of the group to form complex planar prescribed shapes.”
MethodOpen-Loop Collective Assembly Using a Light Field to Power and Control a Phototaxic Mini-Robot Swarm
Bignell, A., Li, L., & Vaughan, R. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794148 · Cited by 1
“Collective assembly is a canonical multi-robot task whereby the robots construct pre-defined structures from either atomic building blocks [1] or the robot bodies themselves [2].”
BackgroundBrain Storm Optimized Swarm Collaboration for Bus Scheduling
Liu, L., Ma, S., & Huang, J. S. (2019) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2019.8891338 · Cited by 1
“However, as shown in [5], without dynamic and local optimization, the formation of even a simple wrench shape could be very slow.”
BackgroundSearch Planning and Analysis for Mobile Targets with Robots
Ye, S., Wong, W. K., & Liu, H. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-38819-5_1 · Cited by 1
The expense of neuro-morpho functional machines
Hallauer, S., & Nitschke, G. (2020) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3377929.3389926 · Cited by 1
Parhizkar, M., Serugendo, G. D. M., Nitschke, J., et al. (2020) · Artificial Life and Robotics · doi:10.1007/s10015-020-00656-w · Cited by 1
Hasenfuss, H. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-67108-2_5 · Cited by 1
Decentralized Brains: A Reference Implementation with Performance Evaluation
Venkatapathy, A. K. R., Gouda, A., ten Hompel, M., et al. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-71061-3_6 · Cited by 1
Molecular Swarm Robot Realized by the Intelligence of a Biomolecular Motor System and DNA
Keya, J. J., Kuzuya, A., & Kakugo, A. (2021) · Seibutsu Butsuri · doi:10.2142/biophys.61.330 · Cited by 1
Distributed Transformations of Hamiltonian Shapes Based on Line Moves
Almethen, A., Michail, O., & Potapov, I. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89240-1_1 · Cited by 1
“A prominent example is [35] in which the transformation relies on an initial central phase to gain some information about the number of entities in the system.”
BackgroundCentralised Connectivity-Preserving Transformations for Programmable Matter: A Minimal Seed Approach
Connor, M., Michail, O., & Potapov, I. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89240-1_4 · Cited by 1
Coverage control of mobile agents using multi-step broadcast control
Darmaraju, S., Kamal, M. A. S., Shanmugavel, M., & Tan, C. P. (2022) · Robotica · doi:10.1017/s0263574722000194 · Cited by 1
Engineering Micromotors by Droplet Microfluidics
Zhu, P., & Wang, L. (2022) · Springer eBooks · doi:10.1007/978-3-030-96462-7_7 · Cited by 1
Designing an Architectural Robot: An Actuated Active Transforming Structure Using Face Detection
Shi, J. F., Wang, Y., & Liu, S. (2020) · Architectural Intelligence · doi:10.1007/978-981-15-6568-7_16 · Cited by 1
Parhizkar, M., Serugendo, G. D. M., Nitschke, J., et al. (2020) · Artificial Life and Robotics · doi:10.1007/s10015-020-00657-9 · Cited by 1
van Beurden, K. (2022) · CRC Press eBooks · doi:10.1201/9781003265924-21 · Cited by 1
Molecular Actuator for Molecular Robots
Kakugo, A. (2022) · Springer eBooks · doi:10.1007/978-981-19-3987-7_5 · Cited by 1
Spontaneous Emergence of Multitasking Robotic Swarms
Zhang, J., Liang, Y., Zhu, S., Xiang, T., Zhu, H., Gu, J. J., Song, W., & Li, T. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011681 · Cited by 1
Introduction to WOSPP: Wave Oriented Swarm Programming Paradigm.Preprint
Varughese, J. C., Hornischer, H., Thenius, R., et al. (2018) · arXiv (Cornell University) · Cited by 1
Ultra-wideband-based navigation for unmanned aerial vehicles
Guo, K. (2018) · doi:10.32657/10220/47471 · Cited by 1
Terminating Distributed Construction of Shapes and Patterns in a Fair Solution of AutomataPreprint
Michail, O. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.01913 · Cited by 1
Distributed on-line reinforcement learning in a swarm of sterically interacting robotsPreprint
Zion, M. Y. B., Bredèche, N., & Dauchot, O. (2021) · arXiv (Cornell University) · Cited by 3
Specian, A., & Yim, M. H. (2020) · Springer eBooks · doi:10.1007/978-3-642-41610-1_148-1 · Cited by 1
Kagan, E., Shvalb, N., & Ben‐Gal, I. (2019) · doi:10.1002/9781119213154.ch0 · Cited by 1
A proposal to improve research in AI algorithm and data governancePreprint
Braga, J., Regateiro, F. S., Stiubiener, I., & Braga, J. C. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/sr7kt · Cited by 1
Environmentally-Driven Morphogenesis of Reconfigurable Floating Robots using Markov ModelsPreprint
Obayashi, N., Meyer, G., Junge, K., & Hughes, J. (2023) · Research Square · doi:10.21203/rs.3.rs-3037225/v1 · Cited by 1
Area Coverage for Swarm Robots Via Inverse Reinforcement LearningPreprint
Chen, M., & Zhang, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4592186 · Cited by 1
Sándor, B., Gros, C., & Manoonpong, P. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1372772 · Cited by 1
Coraggio, M., Salzano, D., & di Bernardo, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-14081-5.00167-7 · Cited by 1
“Applications include swarm robotics (Maffettone, Boldini, di Bernardo & Porfiri 2023, Rubenstein et al. 2014, Gardi et al. 2022), traffic systems (Siri et al. 2021), biological cell populations (de Cesare et al. 2022), and crowd dynamics (Albi et al. 2020).”
Dynamics of mound repair behavior in termitesPreprint
Raja, S. V., Vats, A., Prasad, P., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.19.654859 · Cited by 1
Palmer, D. W., Kirschenbaum, M., & Mustee, E. (2015) · Swarm/Human Blended Intelligence Workshop (SHBI) · doi:10.1109/shbi.2015.7321684
Takashi Gomi and the evolution of embodied AI
Floreano, D. (2014) · Adaptive Behavior · doi:10.1177/1059712314553807
Sitti, M., Ceylan, H., Hu, W., et al. (2015)
Towards realizing a multiple vehicle coordination system
Yuan, Q. B., Shang, H., Zhan, J., et al. (2015) · Chinese Automation Congress · doi:10.1109/cac.2015.7382652
“Researchers in Harvard University made more than a thousand super-mini robots form pictures autonomously [15],[16].”
BackgroundWang, J., & Smith, C. I. (2015) · International Journal of Handheld Computing Research · doi:10.4018/ijhcr.2015070104
A Physical Approach to Swarming
Bouffanais, R. (2015) · SpringerBriefs in complexity · doi:10.1007/978-981-287-751-2_3
Iwase, Y., Suzuki, R., & Arita, T. (2015) · International Journal of Systems Biology and Biomedical Technologies · doi:10.4018/ijsbbt.2015010101
Researchers create 1,000-robot swarm
Zastrow, M. (2014) · Nature · doi:10.1038/nature.2014.15714
Tangible Networks: A Toolkit for Exploring Network Science
Knoop, E., Barter, E., de Villafranca, A. E. M., et al. (2016) · Springer proceedings in complexity · doi:10.1007/978-3-319-29228-1_4
“[23]), where typically robots will communicate wirelessly with other neighbouring robots and the swarm exhibits a global behaviour from the local interactions.”
BackgroundCoalitional Search and Swarm Dynamics
Kagan, E., & Ben-Gal, I. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18604-9
Autonomous identification of local agents in multi-agent robotic swarms
Roggow, A., El-Howayek, G., & Khorbotly, S. (2016) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2016.7535233
“This capability is extremely important as it eliminates the need for tedious individual programming and manipulation of every single robot in a swarm that may comprise hundreds or even thousands of robots [7].”
BackgroundRobotic cell surface mechanics
Hinsch, M., Marée, A. F. M., & Grieneisen, V. A. (2016) · Artificial Life Conference Proceedings · doi:10.1162/978-0-262-33936-0-ch061
A Robot Experiment in Site Selection
Valentini, G. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-53609-5_7
Parallel Manipulation of Millirobot Swarms Using Projected Light Fields
Lawler, C. R., Penskiy, I., Sirken, A., et al. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-50115-4_35
“Previous work on generalizable global pattern formation has required the robots to have a notion of the global pattern they are supposed to form [3], global coordinates of where they are in the space, or a central computer to calculate trajectories even if the sensing is local [4].”
BackgroundStay Cool: Exploring A Group Performed Thermoregulatory Fanning Behavior In Honeybees
Cook, C. N. (2015) · CU Scholar (University of Colorado Boulder)
Art and Engineering Inspired by Swarm Robotics
Yu, Z. (2017) · Rice Research Repository (Rice University)
Controlling Swarms: A Programming Paradigm with Minimalistic CommunicationPreprint
Varughese, J. C., Hornischer, H., Thenius, R., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04202
“…perform complex tasks, various algorithms and methods have been suggested to control a swarm of robots of varying sizes to perform specific actions and tasks in a coordinated manner such as arranging themselves in a particular shape or responding collectively to external cues or stimuli [9, 22, 33].”
BackgroundMethodCollision Free Navigation of a Multi-Robot Team for Intruder InterceptionPreprint
Marzoughi, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02319
“68 applications such as search and rescue, transportation, sensor network deployment, intruder detection and siege and border protection could be addressed [203],[204],[205],[206],[207],[208].”
BackgroundIntegrated control of a robotic group with partial dominance of decision variants
Misyurin, S. Y., Potapov, M. A., & Nelyubin, A. P. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1205/1/012038
Collective Motion with Direct and Indirect Communication
Kagan, E., & Ben‐Gal, I. (2019) · Wiley eBooks · doi:10.1002/9781119213154.ch11
Collective Motion with Shared Environment Map
Kagan, E., & Ben‐Gal, I. (2019) · doi:10.1002/9781119213154.ch10
Experimental Analysis of Programmable Particles
Berdugo, H. D. (2019) · doi:10.22215/etd/2019-13628
Diversification by Self-reinforcement of Preferences and Change of Interaction Type
Iwanaga, S., & Kubo, M. (2019) · Springer eBooks · doi:10.1007/978-981-13-8319-9_3
Statistical Exploration of Distributed Pattern Formation Based on Minimalistic Approach
SUEOKA, Y., Tahara, T., Ishikawa, M., & Osuka, K. (2019) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2019.p0905
Self-assembly near Ground State: Randomly Pack Granular Spheres and Cubes into CrystalPreprint
Amirifar, R., Dong, K., & Yu, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02320
Collective protection and transport in entangled biological and robotic active matterPreprint
Ozkan-Aydin, Y., Goldman, D. I., & Bhamla, M. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.25.114736
“Experimentally, although swarming systems have 28 been successfully realized to collectively accomplish a common 29 goal (30–32), each individual robot is equipped with costly and 30 sophisticated sensors to leverage some degree of centralized 31 control, which is subject to many limitations including low 32 fault tol…”
BackgroundBajaj, V., & Rao, S. (2020) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/ahgm5472
“…approach from: [3] and [4], where a leader robot coordinates other robots to form the desired shape, with constraints on the initial configuration; [10] and [12], where shapes and patterns are built sequentially with the help of seed robots; and [2], where a homogeneous, reactive and memory-less…”
BackgroundMassive Self-Assembly in Grid EnvironmentsPreprint
Chu, W., Zhang, W., Zhao, H., Jin, Z. Q., & Mei, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.05037
“The cause is that the agent stop condition of E-F does not suitable for shapes with holes.”
BackgroundMethodInfluentialA Hardware Architecture and Physical Prototype for General-Purpose Swarm Minirobotics: Proteus II
Moustafa, N., Iglesias, A., & Gálvez, A. (2021) · Springer tracts in nature-inspired computing · doi:10.1007/978-981-16-0662-5_8
Rusek, M., & Dwornicki, G. (2021) · Electronics · doi:10.3390/electronics10131553
GenGrid: A Generalised Distributed Experimental Environmental Grid for Swarm Robotics
Kedia, P., & Rao, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561654
Search and restore: a study of cooperative multi-robot systems
Haire, M. (2021) · Sheffield Hallam University · doi:10.7190/shu-thesis-00395
Xu, G., Cao, Y., Sun, J., et al. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9601534
“The natural quantity superiority of swarms can overcome the shortcomings of single agent, i.e., limited execution-mission capabilities and low resistances of agent-losses [2].”
BackgroundLattice-based Group Enlargement for a Robot Swarm based on Crystal Growth Models
YAMAGISHI, K., & SUZUKI, T. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.01212110
Social Behavior, Cooperation, and Kinship
Breed, M. D., & Moore, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819558-1.00013-0
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
