Cited by 1,308Modular Robots and Swarm IntelligenceDistributed Control Multi-Agent SystemsMicro and Nano Robotics

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

Rubenstein, Michael, Cornejo, Alejandro, Nagpal, Radhika·2014·Science

Open this paper in CoChat — free account

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

Integrity signals

Registry

Crossref · 10.1126/science.1254295

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-Assembly at All Scales
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-citation
Coordinating 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
Programmable matter
Goldstein, S. C., Campbell, J. D., & Mowry, T. C. (2005) · Computer · doi:10.1109/mc.2005.198 · Cited by 327

Show all 22 references

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.

Sorted by citations

Items 501–600 of 1,262

AutoFac: The Perpetual Robot Machine
Nitschke, G., & Howard, D. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3104789 · Cited by 13
Self-Propelled Chemical Garden Tubes
Wang, Q., Knoll, P., & Steinbock, O. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c09088 · Cited by 13
Multi-robot target entrapment using cooperative hierarchical gene regulatory network
Wu, M., Zhu, X., Ma, L., Bao, W., Fan, Z., & Jin, Y. (2023) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2023.101310 · Cited by 13
Nonlinear out-of-plane resonation of a circular dielectric elastomer
Tang, C., Li, B., Liu, L., et al. (2020) · Smart Materials and Structures · doi:10.1088/1361-665x/ab710e · Cited by 13
Self-organizing systems: what, how, and why?
Gershenson, C. (2025) · npj Complexity · doi:10.1038/s44260-025-00031-5 · Cited by 22
Interaction and navigation of robots based on ultrawideband direct chaotic communication
Gulyaev, Y. V., Dmitriev, A. S., Lazarev, V. A., et al. (2016) · Journal of Communications Technology and Electronics · doi:10.1134/s1064226916080040 · Cited by 12
Percolation transition in phase-separating active fluid
Sanoria, M., Chelakkot, R., & Nandi, A. (2022) · Physical review. E · doi:10.1103/physreve.106.034605 · Cited by 12
Coherent movement of error-prone individuals through mechanical coupling
Pratissoli, F., Reina, A., Lopes, Y. K., Pinciroli, C., Miyauchi, G., Sabattini, L., & Groß, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39660-6 · Cited by 14
Hedonic Coalition Formation for Distributed Task Allocation in Heterogeneous Multi-agent System
Wang, L., Qiu, T., Pu, Z., Yi, J., Zhu, J., & Yuan, W. (2024) · International Journal of Control Automation and Systems · doi:10.1007/s12555-022-1182-5 · Cited by 13
“In addition, because of the dynamic and unpredictable nature of tasks, it is difficult to design an agent that can efficiently adapt to all tasks, and it is not cost-effective to equip all sensors or actuators to an agent [9,10].”
Numerical study of collective motion of microswimmers in Giesekus viscoelastic fluids
Zhang, M., Yu, Z., Ouyang, Z., et al. (2024) · Journal of Non-Newtonian Fluid Mechanics · doi:10.1016/j.jnnfm.2024.105245 · Cited by 12
Distributed Reinforcement Learning For Swarm Systems With Reward Machines
Alsadat, S. M., Baharisangari, N., Paliwal, Y., et al. (2024) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc60939.2024.10644549 · Cited by 12
“Swarms find applications in a diverse array of scenarios, ranging from complex shape formation [2], [3] to missions involving exploration and rescue [1], [4], [5].”
Background
Inverse design of tubular energy absorbers formed from the least-symmetric crystallographic DDC surface
Lu, C., Chen, Y., Shi, J., et al. (2025) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2025.113508 · Cited by 12
Large density expansion of a hydrodynamic theory for self-propelled particles
Ihle, T. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02461-4 · Cited by 12
Collectives of Spinning Mobile Microrobots for Navigation and Object Manipulation at the Air-Water Interface
Wang, W., Kishore, V., Koens, L., Lauga, E., & Sitti, M. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593519 · Cited by 12
“Inspired by these natural examples, researchers have constructed artificial collective systems at both macroscopic [4-7] and microscopic scales [8-13].”
Background
HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective
Coucke, N., Heinrich, M. K., Axel Cleeremans, B., & Dorigo, M. (2021) · Swarm Intelligence · doi:10.1007/s11721-021-00199-1 · Cited by 12
Massive Shape Formation in Grid Environments
Chu, W., Zhang, W., Zhao, H., Jin, Z. G., & Mei, H. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3185537 · Cited by 12
“068), and E-F ( × 1 .”
MethodInfluential
Distributed Hierarchical Distribution Control for Very-Large-Scale Clustered Multi-Agent Systems
Saravanos, A. D., Li, Y., & Theodorou, E. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.110 · Cited by 17
A distributed approach to the control of multi-robot systems using XP colonies
Florea, A. G., & Buiu, C. (2017) · Integrated Computer-Aided Engineering · doi:10.3233/ica-170554 · Cited by 12
“A collective algorithm for shape formation is demonstrated for a 1000-Kilobot swarm in [40].”
A Self-organized Shape Formation Method for Swarm Controlling
Bi, Q., & Huang, Y. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8482719 · Cited by 12
Gravity induced formation of spinners and polar order of spherical microswimmers on a surface
Shen, Z., & Lintuvuori, J. S. (2019) · Physical Review Fluids · doi:10.1103/physrevfluids.4.123101 · Cited by 12
“Understanding the key physical principles behind the collective dynamics can be in addition to the understanding of the dynamic order in nature, also inspire the creation of functional active materials [8, 9] or robotic applications where the selfcooperation of small machines leads to large coherent assembly capable o…”
Background
A swarm design paradigm unifying swarm behaviors using minimalistic communication
Varughese, J. C., Hornischer, H., Zahadat, P., Thenius, R., Wotawa, F., & Schmickl, T. (2020) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/ab6ed9 · Cited by 12
“Rubenstein et al (2014) uses a variation of the aforementioned algorithm to assemble various shapes in a self organized manner in a thousand robot swarm.”
Method
Flowing crowds
Ouellette, N. T. (2019) · Science · doi:10.1126/science.aav9869 · Cited by 12
Machine Behaviour (Originally Published 2019 by Springer Nature)
Rahwan, I., Cebrián, M., Obradovich, N., Bongard, J., Bonnefon, J., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2022) · Wiley eBooks · doi:10.1002/9781119815075.ch10 · Cited by 12
Physical Intelligence in Small‐Scale Robots and Machines
Chen, H., & Sitti, M. (2025) · Advanced Materials · doi:10.1002/adma.202510332 · Cited by 15
Swarm coordination of fish-like magnetic soft robots: directed aggregation and shape-adaptive attachment toward efficient drug delivery
Mao, L., Tian, C., Yang, P., Meng, X., Shen, X., Zhang, H., & Xie, H. (2025) · National Science Review · doi:10.1093/nsr/nwaf429 · Cited by 13
Active nematic liquid crystals simulated by particle-based mesoscopic methods
Macías-Durán, J., Duarte-Alaniz, V., & Híjar, H. (2023) · Soft Matter · doi:10.1039/d3sm00481c · Cited by 11
A newly developed chemical locomotory booster for cyborg insect to sustain its activity and to enhance covering performance
Lin, Q., Kai, K., Nguyen, H. D., et al. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.134774 · Cited by 11
3D printing modules for self-assembling space systems
Ellery, A., & Elaskri, A. (2025) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2025.101128 · Cited by 8
Synthesizing Rulesets for Programmable Robotic Self-assembly: A Case Study Using Floating Miniaturized Robots
Haghighat, B., Platerrier, B., Waegeli, L., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44427-7_17 · Cited by 11
“Programmable SA has been demonstrated in [11], [8].”
BackgroundMethod
Flocking of Mobile Agents Using a New Interaction Model: A Cyber-Physical Perspective
Li, H., Peng, J., Zhang, X., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2669438 · Cited by 11
“INTRODUCTION Flocking is a form of collective animal behavior which is believed to emerge from simple local rules of interactions among the individuals [1].”
Background
HoverBots: Precise Locomotion Using Robots That Are Designed for Manufacturability
Nemitz, M. P., Sayed, M. E., Mamish, J., Ferrer, G., Teng, L., McKenzie, R. M., Hero, A. O., Olson, E., & Stokes, A. A. (2017) · Frontiers in Robotics and AI · doi:10.3389/frobt.2017.00055 · Cited by 11
“An excellent starting point is Senanayake review on search and tracking algorithms for swarm robots (Senanayake et al., 2014).”
Background
Control of Large Swarms via Random Finite Set Theory
Doerr, B. G., & Linares, R. (2018) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2018.8430968 · Cited by 11
Decentralised coverage of a large structure using flocking of autonomous agents having a dynamic hierarchy model
Nath, S., Baishya, M., & Ghose, D. (2022) · Autonomous Robots · doi:10.1007/s10514-022-10041-0 · Cited by 11
“Our work is closely related to shape formation algorithms (Wang and Rubenstein 2020; Li et al. 2019; Rubenstein et al. 2014; Jung and Kim 2014), where a ﬂock of agents move to take up a speciﬁed shape.”
Background
Robotic manipulation of thin objects within off-the-shelf parallel grippers with a vibration finger
Nahum, N., & Sintov, A. (2022) · Mechanism and Machine Theory · doi:10.1016/j.mechmachtheory.2022.105032 · Cited by 11
“The mechanism later inspired the design of the Kilobot [38] for swarming behaviour research [39].”
Platforms for industrial cyber-physical systems integration: contradicting requirements as drivers for innovation
Heiß, M., Oertl, A., Sturm, M., et al. (2015) · Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) · doi:10.1109/mscpes.2015.7115405 · Cited by 11
“We know from the field of swarm intelligence [24, 25] that very simple individual devices, in a high number and highly interconnected, can lead to very complex system behavior.”
Background
A gradient-based self-healing algorithm for mobile robot formation
Liu, Z., Ju, J., Chen, W., Fu, X., & Wang, H. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353850 · Cited by 11
“The second category is the density-based self-healing algorithm [7], in which the density of agents is selected as a key index during the topology repairing process.”
BackgroundMethod
Coalition Formation for Multi-agent Pursuit Based on Neural Network
Pei, Z., Piao, S., Souidi, M. E. H., et al. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-018-0893-6 · Cited by 11
Robotic Swarm Dispersion Using Gradient Descent Algorithm
Bayert, J., & Khorbotly, S. (2019) · IEEE International Symposium on Robotic and Sensors Environments · doi:10.1109/rose.2019.8790430 · Cited by 11
Exploring Self-Assembling Behaviors in a Swarm of Bio-micro-robots using Surrogate-Assisted MAP-Elites
Cazenille, L., Bredèche, N., & Aubert-Kato, N. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003047 · Cited by 11
Decentralized Control for Swarm Robots That Can Effectively Execute Spatially Distributed Tasks
Kano, T., Naito, E., Aoshima, T., et al. (2020) · Artificial Life · doi:10.1162/artl_a_00317 · Cited by 11
“While previous swarm robots have been developed for many purposes such as aggregation [3, 4, 7, 13, 25, 33, 41], self-assembly [10, 14, 16, 32], collective transport [26], and foraging through task allocation [21, 35, 38, 40], in this study we design a swarm robotic system that can effectively perform spatially distri…”
Method
Statistical reprogramming of macroscopic self-assembly with dynamic boundaries
Çulha, U., Davidson, Z. S., Mastrangeli, M., & Sitti, M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001272117 · Cited by 11
“Multiagent distributed robotic systems could also benefit from the dynamic boundary control scheme we presented for assembly formation once our passive particles are replaced by mobile robots (32, 34).”
Autoperforation of two-dimensional materials to generate colloidal state machines capable of locomotion
Liu, A. T., Yang, J., LeMar, L. N., Zhang, G., Pervan, A., Murphey, T. D., & Strano, M. S. (2020) · Faraday Discussions · doi:10.1039/d0fd00030b · Cited by 11
Data-driven framework for the adaptive exit selection problem in pedestrian flow: Visual information based heuristics approach
Zhou, Z.-X., Nakanishi, W., & Asakura, Y. (2021) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.126289 · Cited by 11
Tangible tabletops and dual reality for crisis management: case study with mobile robots and dynamic tangible objects
Merrad, W., Habib, L., Héloir, A., et al. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.04.051 · Cited by 11
Toward a theory of collective resource distribution: a study of a dynamic morphogenesis controller
Zahadat, P., & Hofstadler, D. N. (2019) · Swarm Intelligence · doi:10.1007/s11721-019-00174-x · Cited by 11
“Most of those methods generate defined shapes and are mainly based on diffusion mechanisms, encodings of the desired shapes, and some form of rules determining the behavior of the robots (Meng et al. 2013; Rubenstein et al. 2014; Slavkov et al. 2018; Stoy and Nagpal 2007).”
Background
Collective Decision-Making
Hamann, H. (2018) · Springer eBooks · doi:10.1007/978-3-032-10584-4_8 · Cited by 11
Feedback Control of Active Matter
Takatori, S. C., Quah, T., & Rawlings, J. B. (2025) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-042424-043926 · Cited by 10
A geometric condition for robot-swarm cohesion and cluster–flock transition
Casiulis, M., Arbel, E., van Waes, C., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502211122 · Cited by 10
Robot self-assembly as adaptive growth process: Collective selection of seed position and self-organizing tree-structures
Soorati, M. D., & Hamann, H. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759845 · Cited by 10
“ticipated structures [3], [5] and works that focus on adaptive growth processes where only certain qualities of the resulting structure are specified [20], [21].”
BackgroundMethod
An RSS-based triangulation method for robot tracking in robotic swarms
Khorbotly, S., El-Howayek, G., Morrison, A., et al. (2017) · International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS) · doi:10.1109/marss.2017.8001928 · Cited by 10
SwarmCom: an infra-red-based mobile ad-hoc network for severely constrained robots
Trenkwalder, S. M., Esnaola, I., Lopes, Y. K., et al. (2019) · Autonomous Robots · doi:10.1007/s10514-019-09873-0 · Cited by 10
“The high robot densities found in some swarm robotics systems, with hundreds to thousands of nodeswithin a 3m radius (Mondada et al. 2009; Rubenstein et al. 2014), exceed the capabilities of most wireless networks.”
Background
Effective temperature and dissipation of a gas of active particles probed by the vibrations of a flexible membrane
Boudet, J. F., Jagielka, J., Guérin, T., et al. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.l042006 · Cited by 10
Multicriteria adaptation of robotic groups to dynamically changing conditions
Misyurin, S. Y., Nelyubin, A. P., & Ivlev, V. I. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/788/1/012027 · Cited by 10
Beyond AI: Multi-Intelligence (MI) Combining Natural and Artificial Intelligences in Hybrid Beings and Systems
Fox, S. (2017) · Technologies · doi:10.3390/technologies5030038 · Cited by 10
Automatic synthesis of rulesets for programmable stochastic self-assembly of rotationally symmetric robotic modules
Haghighat, B., & Martinoli, A. (2017) · Swarm Intelligence · doi:10.1007/s11721-017-0139-4 · Cited by 10
“Programmable SA has been demonstrated in Rubenstein et al. (2014), and Klavins (2007) where active modules self-assemble into predefined desired 2D structures following a set of assembly rules.”
BackgroundMethod
Concept design and cluster control of advanced space connectable intelligent microsatellite
Wang, X.-H., Li, S., & She, Y. (2017) · Acta Astronautica · doi:10.1016/j.actaastro.2017.09.024 · Cited by 10
An Affordable Set of Control System Laboratories Using A Low-Cost Robotic Platform
Jaramillo, A. F. R., Segura-Quijano, F. E., & Quijano, N. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2018.2843888 · Cited by 10
“The self-organized system research group from Harvard University has created Kilobot [23], a really low-cost platform ($14 only parts), oriented to demonstrate collective behaviors over thousands of robots, such as collective formation [27].”
Background
Engineered self-organization for resilient robot self-assembly with minimal surprise
Kaiser, T. K., & Hamann, H. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.103293 · Cited by 10
“Self-assembly behaviors were already implemented on robot swarms of up to one thousand Kilobots [22].”
Method
Effective Mobile Target Searching Using Robots
Wong, W. K., Ye, S., Liu, H., et al. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01628-x · Cited by 10
An iterative optimization approach for multi-robot pattern formation in obstacle environment
Zhang, F., Wang, T., Li, Q., et al. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103645 · Cited by 10
Distributed Event-Triggered Flocking Control of Lagrangian Systems
Aranda‐Escolástico, E., Colombo, L. J., & Guinaldo, M. (2021) · IEEE Control Systems Letters · doi:10.1109/lcsys.2021.3136761 · Cited by 10
“These natural collective behaviors can be leveraged in multirobot systems to safely transport large cohesive groups of robots within a workspace [1].”
Background
Rectification of chiral active particles driven by transversal temperature difference
Ai, B.-Q., Li, J.-J., Li, Z.-Q., et al. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5096323 · Cited by 10
Linear Quadratic Optimal Control of Time-Invariant Linear Networks With Selectable Input Matrix
Hao, Y. A., Wang, T., Li, G., et al. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2953218 · Cited by 10
“Also, when controlling swarm robots, the connections between control sources and robots are also designable [14].”
Background
Collective Cognition and Sensing in Robotic Swarms via an Emergent Group-Mind
Otte, M. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-50115-4_72 · Cited by 9
“, [24], we manually place robots in the environment to conserve battery life and memory space.”
Method
Development and Improvement of a Piezoelectrically Driven Miniature Robot
Wu, G., Wang, Z., Wu, Y. X., et al. (2024) · Biomimetics · doi:10.3390/biomimetics9040226 · Cited by 9
Macroscopic, artificial active matter
Ning, L., Zhu, H., Yang, J., Zhang, Q., Liu, P., Ni, R., & Zheng, N. (2024) · National Science Open · doi:10.1360/nso/20240005 · Cited by 10
Macroscopic Self‐Assembly Realized by Polymer Brush─A Thickness‐Dependent Rule for Rapid Wet Adhesion
Lin, C., Hou, F., Zhang, Q., et al. (2024) · Small · doi:10.1002/smll.202404526 · Cited by 9
Dual Responsive Magnetic DCR3 Nanoparticles: A New Strategy for Efficiently Targeting Hepatocellular Carcinoma
Jia, L., Dai, Y., Xu, Y., Sun, H., Gao, H. J., Hao, H., Wang, L., Xu, J., Shang, J., Li, G., et al. (2024) · Small · doi:10.1002/smll.202402909 · Cited by 9
Microrobots for pulmonary drug delivery
Li, Z., Luan, H., Fang, Z., Ding, S., Kobrin, R., Fang, R. H., Zhang, L., & Wang, J. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00381-8 · Cited by 14
Computing Coordinated Motion Plans for Robot Swarms: The CG:SHOP Challenge 2021
Fekete, S. P., Keldenich, P., Krupke, D., et al. (2022) · ACM Journal of Experimental Algorithmics · doi:10.1145/3532773 · Cited by 9
“How can each agent choose an eicient route that avoids collisions with other agents as they simultaneously move to their destinations? These basic questions arise in many applications, such as ground swarm robotics [17, 18], aerial swarm robotics [3, 26], air traic control [5], and vehicular traic networks [10, 19].”
Background
Learning to Swarm with Knowledge-Based Neural Ordinary Differential Equations
Jiahao, T. Z., Pan, L., & Hsieh, M. A. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811997 · Cited by 9
Controlling Displacement Fields in Polar Willis Solids via Gauge Transformations
Chen, Y., & Haberman, M. R. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.147201 · Cited by 10
Design principles for transporting vesicles with enclosed active particles (a)
Uplap, S., Hagan, M. F., & Baskaran, A. (2023) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/acfab9 · Cited by 9
Mobility-induced order in active XY spins on a substrate
Haldar, A., Sarkar, A., Chatterjee, S., et al. (2023) · Physical review. E · doi:10.1103/physreve.108.l032101 · Cited by 9
“We hope our theory will induce future experiments to measure ⟨θ(2)⟩ and σ(N0) in self-assembled spinners [60], rotor assemblies [59, 61, 62] and programmable magnetic cilia carpets [16, 18].”
Method
Complex motion of steerable vesicular robots filled with active colloidal rods
Lee, S. Y., Schönhöfer, P. W. A., & Glotzer, S. C. (2023) · Scientific Reports · doi:10.1038/s41598-023-49314-8 · Cited by 9
Distributed formation for robotic swarms considering their crossing motion
Kobayashi, Y., Endo, T., & Matsuno, F. (2018) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2018.09.012 · Cited by 9
The near and far of a pair of magnetic capillary disks
Koens, L., Wang, W., Sitti, M., et al. (2019) · Soft Matter · doi:10.1039/c8sm02215a · Cited by 9
Study on Electrostatic Inchworm Motor Device for a Heterogeneous Integrated Microrobot System
Saito, K., Contreras, D. S., Takeshiro, Y., et al. (2019) · Transactions of The Japan Institute of Electronics Packaging · doi:10.5104/jiepeng.12.e18-009-1 · Cited by 9
“A number of centimeter-scale robots have been constructed from miniaturized electrical components integrated with sensors, actuators, power sources, and controllers,[6, 9] and several bio-”
Background
Trajectory Planning for the Shapeshifting of Autonomous Surface Vessels
Gheneti, B., Park, S., Kelly, R., et al. (2019) · International Symposium on Multi-Robot and Multi-Agent Systems (MRS) · doi:10.1109/mrs.2019.8901099 · Cited by 9
Swarm robotics:design and implementation
Abuelhaija, A., Jebrein, A., & Baldawi, T. (2020) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v10i2.pp2173-2181 · Cited by 9
“M. Rubenstein et al. [14] created a large swarm of programmed robots that can form collaborations using only local information.”
Background
Pinning synchronization of weighted bipartite networks with time-varying delays via aperiodic intermittent control
Shen, Y., Shi, J., & Cai, S. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.123568 · Cited by 9
Designing Dynamical Molecular Systems with the PEN Toolbox
Aubert-Kato, N., & Cazenille, L. (2020) · New Generation Computing · doi:10.1007/s00354-020-00089-w · Cited by 9
“This can be observed in biology [1, 14], which has spurred the design of self-assembling bio-inspired robotic swarms [24, 34, 44, 51].”
Background
Swarm robotics – a case study: bat robotics
Iglesias, A., Gálvez, A., & Suárez, P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819714-1.00026-9 · Cited by 9
Multi-Role UAV Swarm Behaviors for Wide Area Search using Emergent Intelligence
Arnold, R. D., Mezzacappa, E. S., Jablonski, J., et al. (2020) · World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) · doi:10.1109/worlds450073.2020.9210392 · Cited by 9
Smoothed-Particle-Hydrodynamics for the Control of Robotic Swarms, and Parametric Associations
Mitikiri, Y. B., Silic, M., & Mohseni, K. (2021) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2021.3092823 · Cited by 9
Curvature-controlled geometrical lensing behavior in self-propelled colloidal particle systems
Schönhöfer, P. W. A., & Glotzer, S. C. (2022) · Soft Matter · doi:10.1039/d2sm01012g · Cited by 9
“In particular, the combination between fluidization and the mutable onset of phase separation suggests curvature being a promising tool to control active particle systems for colloidal robotic applications without requiring highly complex particle interactions [49, 50].”
Background
On finite-/fixed-time synchronization of multi-weighted dynamical networks: a new unified control approach
Shi, J., Zhou, P., Cai, S., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07979-8 · Cited by 9
Distributed control for geometric pattern formation of large-scale multirobot systems
Giusti, A., Maffettone, G. C., Fiore, D., et al. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1219931 · Cited by 9
“This is crucial in swarm robotics, where it can be generally cumbersome, or prohibitively expensive, to equip all agents with GPS sensors and communication capabilities, e.g., Rubenstein et al. (2014).”
Background
Characterization and validation of a novel robotic system for fluid-mediated programmable stochastic self-assembly
Haghighat, B., & Martinoli, A. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759431 · Cited by 9
Terminating distributed construction of shapes and patterns in a fair solution of automata
Michail, O. (2017) · Distributed Computing · doi:10.1007/s00446-017-0309-z · Cited by 9
Rearranging agents in a small space using global controls
Zhang, Y., Chen, X., Qi, H., et al. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206202 · Cited by 9
“[20] designed an algorithm to control kilobots to selfassemble shapes in 2D.”
Method
Crossover between parabolic and hyperbolic scaling, oscillatory modes, and resonances near flocking
Bonilla, L. L., & Trenado-Yuste, C. (2018) · Physical review. E · doi:10.1103/physreve.98.062603 · Cited by 9
“Flocking phenomena occurs in bacteria [1–3], spermatozoa [4], insects [5, 6], birds [7–10], animals [11], active gels [12, 13], or interacting robots [14].”
Background
Algorithms for Task Allocation in Homogeneous Swarm of Robots
Jha, D. K. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594052 · Cited by 9
“Some important examples are fleets of unmanned vehicles deployed in ocean bed for the purposes of mine-hunting or surveillance or drug delivery techniques in humans using micro-scale robots at pre-specified rate and locations [1], [2].”
Background
AutoMoDe-Cedrata: Automatic Design of Behavior Trees for Controlling a Swarm of Robots with Communication Capabilities
Kuckling, J., van Pelt, V., & Birattari, M. (2022) · SN Computer Science · doi:10.1007/s42979-021-00988-9 · Cited by 8
Distributed Line Formation Control in Swarm Robots
Jiang, Z., Wang, X., & Yang, J. (2018) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2018.8812317 · Cited by 9
“One of the most famous article was published in the journal Science in 2014, the authors[3] comes from Harvard University, and they made 1024 Kilobot self-organized robots which each robot get the local information and finally achieve the predefined formation.”
Background
Deriving minimal sensory configurations for evolved cooperative robot teams
Watson, J., & Nitschke, G. (2015) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2015.7257271 · Cited by 8
Naïve creature learns to cross a highway in a simulated CA-like environment
Ławniczak, A. T., Stefano, B. N. D., & Ernst, J. B. (2014) · doi:10.1109/ia.2014.7009455 · Cited by 8
Rapid Information Transfer in Swarms Under Update-Rate-Bounds Using Delayed Self-Reinforcement
Devasia, S. (2019) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4042949 · Cited by 8
Planar Orientation Control and Torque Maximization Using a Swarm With Global Inputs
Shahrokhi, S., Lin, L. S., & Becker, A. T. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2925908 · Cited by 8
“The Kilobot, from [23], [24], is a low-cost robot designed for testing collective algorithms with large numbers of robots.”
Overload wave-memory induces amnesia of a self-propelled particle
Hubert, M., Perrard, S., Vandewalle, N., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31736-z · Cited by 8
“(5), (see SI) while the correlated chain-like distribution created by walkers presents a sublinear scaling Mep with p= 0.”
Background

Authors: Rubenstein, M., Cornejo, A., & Nagpal, R.
Affiliations: Harvard University (US)
Corresponding author: Rubenstein, Michael
ORCID: Rubenstein, Michael
Cornejo, Alejandro
Nagpal, Radhika
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2014-08-15
Volume: 345
Issue: 6198
Pages: 795-799
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.1254295
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

Programmable self-assembly in a thousand-robot swarm

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks