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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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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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Bechinger, C., Leonardo, R. D., Löwen, H., Reichhardt, C. M., Volpe, G., & Volpe, G. (2016) · Reviews of Modern Physics · doi:10.1103/revmodphys.88.045006 · Cited by 2,931
“t al., 2013; Palacci et al., 2013), they still consider only one kind of microswimmer at a time. Nevertheless, we know from recent experiments in robotics (Halloy et al., 2007; Mijalkov et al., 2016; Rubenstein et al., 2014; Volpe and Wehr, 2016) and biology (Gravish et al., 2015; Tennenbaum et al., 2016) that complex…”
BackgroundRahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364
Tang, J., Liu, G., & Pan, Q. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1004129 · Cited by 1,006
Biomedical Applications of Untethered Mobile Milli/Microrobots
Sitti, M., Ceylan, H., Hu, W., Giltinan, J., Turan, M., Yim, S., & Diller, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2385105 · Cited by 897
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Xie, H., Sun, M., Fan, X., Lin, Z., Chen, W., Wang, L., Dong, L., & He, Q. (2019) · Science Robotics · doi:10.1126/scirobotics.aav8006 · Cited by 772
Reconfiguring active particles by electrostatic imbalance
Yan, J. Q., Han, M., Zhang, J., Xu, C., Luijten, E., & Granick, S. (2016) · Nature Materials · doi:10.1038/nmat4696 · Cited by 585
Thubagere, A. J., Li, W., Johnson, R. F., Chen, Z., Doroudi, S., Lee, Y. L., Izatt, G., Wittman, S., Srinivas, N., Woods, D., et al. (2017) · Science · doi:10.1126/science.aan6558 · Cited by 559
The rise of intelligent matter
Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P. (2021) · Nature · doi:10.1038/s41586-021-03453-y · Cited by 526
Programming self-organizing multicellular structures with synthetic cell-cell signaling
Toda, S., Blauch, L. R., Tang, S. K. Y., Morsut, L., & Lim, W. A. (2018) · Science · doi:10.1126/science.aat0271 · Cited by 470
Swarm Robotic Behaviors and Current Applications
Schranz, M., Umlauft, M., Sende, M., & Elmenreich, W. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00036 · Cited by 485
“In later work, Rubenstein et al. (2014b) demonstrate this on a large swarm of physical robots as described above.”
BackgroundMethodLight‐Fueled Microscopic Walkers
Zeng, H., Wasylczyk, P., Parmeggiani, C., Martella, D., Burresi, M., & Wiersma, D. S. (2015) · Advanced Materials · doi:10.1002/adma.201501446 · Cited by 452
Ultra-extensible ribbon-like magnetic microswarm
Yu, J., Wang, B., Du, X., Wang, Q., & Zhang, L. (2018) · Nature Communications · doi:10.1038/s41467-018-05749-6 · Cited by 423
PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning
Sartoretti, G., Kerr, J. H., Shi, Y., Wagner, G., Kumar, T. K. S., Koenig, S., & Choset, H. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2903261 · Cited by 426
“Given the rapid development of affordable robots with embedded sensing and computation capabilities, manufacturing applications will soon regularly involve the deployment of thousands of robots [1], [2].”
BackgroundSwarm Robotics: Past, Present, and Future [Point of View]
Dorigo, M., Theraulaz, G., & Trianni, V. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3072740 · Cited by 431
“Kilobots were conceived to support the first demonstration of a large robot swarm designed for shape formation [31] and have been later used for several successful studies, allowing swarm robotics to be demonstrated in physical settings with hundreds of robots [32]–[34].”
BackgroundHuman Interaction With Robot Swarms: A Survey
Kolling, A., Walker, P., Chakraborty, N., Sycara, K., & Lewis, M. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2015.2480801 · Cited by 424
Signal propagation in complex networks
Ji, P., Ye, J., Mu, Y., Lin, W. J., Tian, Y., Hens, C., Perc, M., Tang, Y. S., Sun, J., & Kurths, J. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.03.005 · Cited by 375
Implicit coordination for 3D underwater collective behaviors in a fish-inspired robot swarm
Berlinger, F., Gauci, M., & Nagpal, R. (2021) · Science Robotics · doi:10.1126/scirobotics.abd8668 · Cited by 362
Self-citationInformation processing via physical soft body
Nakajima, K., Hauser, H., Li, T., & Pfeifer, R. (2015) · Scientific Reports · doi:10.1038/srep10487 · Cited by 324
Particle robotics based on statistical mechanics of loosely coupled components
Li, S., Batra, R., Brown, D., Chang, H.-D., Ranganathan, N., Hoberman, C., Rus, D. L., & Lipson, H. (2019) · Nature · doi:10.1038/s41586-019-1022-9 · Cited by 319
Machine learning for active matter
Cichos, F., Gustavsson, K., Mehlig, B., & Volpe, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0146-9 · Cited by 323
Levin, M. G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02688 · Cited by 334
“Similar approaches are being taken with respect to swarms of insects, robots, and human beings (Couzin, 2009; Deisboeck and Couzin, 2009; Gomes et al., 2013; Rubenstein et al., 2014).”
ResultAguilar, J., Zhang, T., Qian, F., Kingsbury, M., McInroe, B. W., Mazouchova, N., Li, C., Maladen, R. D., Gong, C., Travers, M. J., et al. (2016) · Reports on Progress in Physics · doi:10.1088/0034-4885/79/11/110001 · Cited by 295
Reflections on the future of swarm robotics
Dorigo, M., Theraulaz, G., & Trianni, V. (2020) · Science Robotics · doi:10.1126/scirobotics.abe4385 · Cited by 282
“While technological innovations are pushing the boundaries towards ever smaller scales [5,6] and larger numbers [7,8], the path towards real-word applications is still long and arduous.”
BackgroundDesigning minimal and scalable insect-inspired multi-locomotion millirobots
Zhakypov, Z., Mori, K., Hosoda, K., et al. (2019) · Nature · doi:10.1038/s41586-019-1388-8 · Cited by 268
Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment
McGuire, K., Wagter, C. D., Tuyls, K., Kappen, H. J., & de Croon, G. (2019) · Science Robotics · doi:10.1126/scirobotics.aaw9710 · Cited by 280
“Moreover, swarms of robots have been demonstrated in applications ranging from constructing small preplanned structures (6) to forming shapes with their own bodies (7, 8) and to performing tasks such as dispersion, aggregation, and surveillance (9).”
BackgroundShepherd, D. A., & Majchrzak, A. (2022) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2022.106227 · Cited by 329
Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective
Wang, H., Zhao, H., Zhang, J., Ma, D., Li, J., & Wei, J. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2962207 · Cited by 276
Yang, L., Jiang, J., Gao, X., Wang, Q., Dou, Q., & Zhang, L. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00482-8 · Cited by 259
“BR = [ Bx, By, Bz T = [af sin(2πωt), bf, 0]T (2)”
BackgroundMethodInfluentialZhang, C., Zhou, L., & Li, Y. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3321997 · Cited by 238
“The finite-state automaton with three primitive collective behaviors is conducted for the thousand-robot swarm [2].”
External Power-Driven Microrobotic Swarm: From Fundamental Understanding to Imaging-Guided Delivery
Wang, Q., & Zhang, L. (2021) · ACS Nano · doi:10.1021/acsnano.0c07753 · Cited by 233
Rotating robots move collectively and self-organize
Scholz, C., Engel, M., & Pöschel, T. (2018) · Nature Communications · doi:10.1038/s41467-018-03154-7 · Cited by 214
“We find a good fit with an exponential decay, v / expð tγT=MÞ, similar to a particle suspended in a viscous fluid, which confirms that the assumption of a velocity-dependent frictional force in the Langevin equations (1) and (2) is justified.”
BackgroundYang, X., Xu, L., Lin, P., Zhong, W., Bai, Y., Luo, J., Chen, J., & Wang, Z. L. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.03.054 · Cited by 215
Weak synchronization and large-scale collective oscillation in dense bacterial suspensions
Chen, C., Liu, S., Shi, X.-Q., et al. (2017) · Nature · doi:10.1038/nature20817 · Cited by 209
Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
Orr, J., & Dutta, A. (2023) · Sensors · doi:10.3390/s23073625 · Cited by 244
“Infrared sensors can be used for communication in such a swarm system [215].”
BackgroundSlavkov, I., Carrillo-Zapata, D., Carranza, N., Diego, X., Jansson, F., Kaandorp, J. A., Hauert, S., & Sharpe, J. A. (2018) · Science Robotics · doi:10.1126/scirobotics.aau9178 · Cited by 207
Asokan, N., Brasser, F., Ibrahim, A., et al. (2015) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2810103.2813670 · Cited by 202
“An important property of swarms is that each device can communicate only with its direct neighbors [13, 19, 45].”
BackgroundCompact single-shot metalens depth sensors inspired by eyes of jumping spiders
Guo, Q., Shi, Z., Huang, Y., Alexander, E., Qiu, C., Capasso, F., & Zickler, T. E. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912154116 · Cited by 200
Bio-inspired self-organising multi-robot pattern formation: A review
Oh, H., Shirazi, A. R., Sun, C., & Jin, Y. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.12.006 · Cited by 199
“In the Kilobot project [165], [175], Rubenstein et al.”
BackgroundMethodActive colloids with collective mobility status and research opportunities
Zhang, J., Luijten, E., Grzybowski, B. A., & Granick, S. (2017) · Chemical Society Reviews · doi:10.1039/c7cs00461c · Cited by 200
Programmable Collective Behavior in Dynamically Self‐Assembled Mobile Microrobotic Swarms
Yigit, B., Alapan, Y., & Sitti, M. (2019) · Advanced Science · doi:10.1002/advs.201801837 · Cited by 199
Dong, X., & Sitti, M. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920903107 · Cited by 198
“systems (Rubenstein et al., 2014; Yang et al., 2018), and modular robotics (Daudelin et al.”
BackgroundAdaptive synchronization of multi-agent systems with resilience to communication link faults
Chen, C., Xie, K., Lewis, F. L., Xie, S., & Fierro, R. (2019) · Automatica · doi:10.1016/j.automatica.2019.108636 · Cited by 197
Modular Reconfigurable Robotics
Seo, J., Paik, J. K., & Yim, M. H. (2018) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-053018-023834 · Cited by 182
“Systems with a thousand modules have been shown (68) which showed that practical issues such as how do you program so many modules, and how do you recharge their batteries need to be addressed.”
BackgroundMotion Control in Magnetic Microrobotics: From Individual and Multiple Robots to Swarms
Yang, L., & Zhang, L. (2020) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-032720-104318 · Cited by 181
Microrobot collectives with reconfigurable morphologies, behaviors, and functions
Gardi, G., Ceron, S., Wang, W., Petersen, K. H., & Sitti, M. (2022) · Nature Communications · doi:10.1038/s41467-022-29882-5 · Cited by 178
Gravish, N., & LAUDER, G. V. (2018) · Journal of Experimental Biology · doi:10.1242/jeb.138438 · Cited by 164
“The decreasing cost of new aerial drones and swarm robot platforms such as the kilobots (Rubenstein et al., 2014) will likely increase the adoption of robot experiments by collective behavior biologists.”
BackgroundPhototactic Flocking of Photochemical Micromotors
Mou, F., Zhang, J., Wu, Z. Y., Du, S., Zhang, Z., Xu, L., & Guan, J. (2019) · iScience · doi:10.1016/j.isci.2019.07.050 · Cited by 156
Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems
Valentini, G., Ferrante, E., Hamann, H., & Dorigo, M. (2015) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-015-9323-3 · Cited by 155
“The same and similar robots have successfully been used in swarms sized up to thousands to complete tasks such as aggregation [17], collective transport [35] and self-assembly [36].”
MethodA Critical Review of Communications in Multi-robot Systems
Gielis, J., Shankar, A., & Prorok, A. (2022) · Current Robotics Reports · doi:10.1007/s43154-022-00090-9 · Cited by 160
“A variety of transmission media (e.g., infrared) are used for such near-field communication schemes [13, 14].”
Background3D M-Blocks: Self-reconfiguring robots capable of locomotion via pivoting in three dimensions
Romanishin, J. W., Gilpin, K. W., Claici, S., et al. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139450 · Cited by 149
“Recent work on the Kilobots [5] has shown the promise of large robotic swarms, which can only be realized with a combination of simple hardware and robust algorithms.”
BackgroundEmergence of robust self-organized undulatory swimming based on local hydrodynamic force sensing
Thandiackal, R., Melo, K., Paez, L., Herault, J., Kano, T., Akiyama, K., Boyer, F., Ryczko, D., Ishiguro, A., & Ijspeert, A. J. (2021) · Science Robotics · doi:10.1126/scirobotics.abf6354 · Cited by 153
DNA-assisted swarm control in a biomolecular motor system
Keya, J. J., Ryuhei, S., Kabir, A. M. R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02778-5 · Cited by 143
Microrobotic swarms for selective embolization
Law, J., Wang, X., Luo, M., Xin, L., Du, X., Dou, W., Wang, T., Shan, G., Wang, Y., Song, P., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm5752 · Cited by 148
Coupled liquid crystalline oscillators in Huygens’ synchrony
Vantomme, G., Elands, L. C. M., Gelebart, A. H., Meijer, E. W., POGROMSKY, A. Y., Nijmeijer, H., & Broer, D. J. (2021) · Nature Materials · doi:10.1038/s41563-021-00931-6 · Cited by 145
Model predictive flocking control for second-order multi-agent systems with input constraints
Zhang, H., Cheng, Z., Chen, G., & Li, C. (2015) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2015.2418871 · Cited by 140
“[1], [3]–[17].”
Review of methodologies and tasks in swarm robotics towards standardization
Nedjah, N., & Silva, L. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.100565 · Cited by 144
A Survey on Swarm Microrobotics
Yang, L., Yu, J., Yang, S., Wang, B., Nelson, B. J., & Zhang, L. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3111788 · Cited by 144
Swarming and collective migration of micromotors under near infrared light
Deng, Z., Mou, F., Tang, S., Xu, L., Luo, M., & Guan, J. (2018) · Applied Materials Today · doi:10.1016/j.apmt.2018.08.004 · Cited by 134
Ambrosin, M., Conti, M., Ibrahim, A., et al. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978335 · Cited by 133
“However, devices in G are able to identify and communicate to their direct neighbors, which is a minimal assumption in such networks [31].”
BackgroundTechnology Roadmap of Micro/Nanorobots
Ju, X., Chen, C., Oral, Ç. M., Sevim, S., Golestanian, R., Sun, M., Bouzari, N., Lin, X., Urso, M., Nam, J. S., et al. (2025) · ACS Nano · doi:10.1021/acsnano.5c03911 · Cited by 143
Live from under the lens: exploring microbial motility with dynamic imaging and microfluidics
Son, K., Brumley, D. R., & Stocker, R. (2015) · Nature Reviews Microbiology · doi:10.1038/nrmicro3567 · Cited by 131
Tubular Micro/Nanomachines: From the Basics to Recent Advances
Xu, B., Zhang, B., Wang, L., Huang, G., & Mei, Y. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201705872 · Cited by 128
Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
Jang, I., Shin, H.-S., & Tsourdos, A. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2858292 · Cited by 129
Reconfigurable emergent patterns in active chiral fluids
Zhang, B., Sokolov, A., & Snezhko, A. (2020) · Nature Communications · doi:10.1038/s41467-020-18209-x · Cited by 124
Circular swimming motility and disordered hyperuniform state in an algae system
Huang, M., Hu, W., Yang, S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2100493118 · Cited by 123
“Phys Rev Lett, 93(9):098103, August 2004.”
InfluentialWhen less is more: Robot swarms adapt better to changes with constrained communication
Talamali, M. S., Saha, A., Marshall, J. A. R., & Reina, A. (2021) · Science Robotics · doi:10.1126/scirobotics.abf1416 · Cited by 131
Wang, H., & Pumera, M. (2020) · Chemical Society Reviews · doi:10.1039/c9cs00877b · Cited by 121
Fields, C., & Levin, M. (2022) · Entropy · doi:10.3390/e24060819 · Cited by 121
From animal collective behaviors to swarm robotic cooperation
Duan, H., Huo, M., & Fan, Y. (2023) · National Science Review · doi:10.1093/nsr/nwad040 · Cited by 133
“KilobotrobotsdesignedbyHarvard Universityrealizedprogrammableself-assemblyof complicatedtwo-dimensionalshapesinathousand-scale swarm through local interactions and cooperative algorithms [82].”
BackgroundAdaptive Morphology: A Design Principle for Multimodal and Multifunctional Robots
Mintchev, S., & Floreano, D. (2016) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2016.2580593 · Cited by 119
Activity induced synchronization: Mutual flocking and chiral self-sorting
Levis, D., Pagonabarraga, I., & Liebchen, B. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.023026 · Cited by 115
“[8] M Rubenstein, A Cornejo, and R Nagpal.”
BackgroundMechanics of fire ant aggregations
Tennenbaum, M., Liu, Z., Hu, D. L., et al. (2015) · Nature Materials · doi:10.1038/nmat4450 · Cited by 114
Dynamic and programmable self-assembly of micro-rafts at the air-water interface
Wang, W., Giltinan, J., Zakharchenko, S., & Sitti, M. (2017) · Science Advances · doi:10.1126/sciadv.1602522 · Cited by 113
“However, their onboard batteries, sensors, and processors are difficult to be miniaturized to submillimeter scale (29).”
BackgroundGomes, A., Rubens, C., Braley, S., et al. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858519 · Cited by 113
“Similarly, Rubenstein et al. proposed a thousand-robot swarm capable of self-assembling into larger, potentially interactive, shapes [39].”
BackgroundProbabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents
Bandyopadhyay, S., Chung, S., & Hadaegh, F. Y. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705044 · Cited by 110
“1) [2]–[8] or exploring an unknown environment [9]–[14].”
BackgroundBicomponent Supramolecular Architectures at the Vacuum–Solid Interface
Bouju, X., Mattioli, C., Franc, G., Pujol, A. R., & Gourdon, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00389 · Cited by 111
Jin, D., Yu, J., Yuan, K., & Zhang, L. (2019) · ACS Nano · doi:10.1021/acsnano.9b02139 · Cited by 111
A survey on recent progress in control of swarm systems
Zhu, B., Xie, L., Han, D., Meng, X., & Teo, R. (2017) · Science China Information Sciences · doi:10.1007/s11432-016-9088-2 · Cited by 110
“Experiments of integrated flocking, formation and localization implemented with large swarm of robots can be found in [147], where 1024 robots named Kilobot are programmed to perform swarming and self-assembling by using only limited local information.”
Controlling multiple microrobots: recent progress and future challenges
Chowdhury, S., Jing, W., & Cappelleri, D. J. (2015) · Journal of Micro-Bio Robotics · doi:10.1007/s12213-015-0083-6 · Cited by 109
“[17] have a thousand centimeter scale robot (Kilobot) swarm each with onboard power, sensing, and computation capability to study the swarm behavior in nature.”
BackgroundShields, C. W., & Velev, O. D. (2017) · Chem · doi:10.1016/j.chempr.2017.09.006 · Cited by 109
Learning Without Neurons in Physical Systems
Stern, M., & Murugan, A. (2023) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-040821-113439 · Cited by 133
Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach
Hao, C. H. E. N., Wang, X., Shen, L., et al. (2020) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2020.03.006 · Cited by 106
Gao, C., He, X. H., Dong, H., Liu, H., & Lyu, G. (2022) · International Journal of Systems Science · doi:10.1080/00207721.2022.2056772 · Cited by 107
“It has been experimentally demonstrated that MASs can accomplish complex swarm tasks only through local interactions between individuals, making the central node unnecessary (S. Li et al., 2019; Rubenstein et al., 2014).”
BackgroundMagnetic Active Matter Based on Helical Propulsion
Mandal, P., Patil, G., Kakoty, H., & Ghosh, A. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00315 · Cited by 102
Embodied Evolution in Collective Robotics: A Review
Bredèche, N., Haasdijk, E., & Prieto, A. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00012 · Cited by 114
Tri-Co Robot: a Chinese robotic research initiative for enhanced robot interaction capabilities
Ding, H., Yang, X., Zheng, N., Li, M., Yinan, L., & Wu, H. (2017) · National Science Review · doi:10.1093/nsr/nwx148 · Cited by 102
Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy
Rossi, F., Bandyopadhyay, S., Wolf, M. T., & Pavone, M. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.07.097 · Cited by 99
“We present a non-exhaustive list: the Kilobot algorithm achieves complex two-dimensional shapes and was demonstrated on a thousand-agent testbed (Rubenstein et al., 2014); the Op-timotaxis source-searching algorithm is inspired by the run and tumble behaviors of bacteria (Mesquita et al., 2008);…”
MethodSelf-reconfigurable multilegged robot swarms collectively accomplish challenging terradynamic tasks
Ozkan-Aydin, Y., & Goldman, D. I. (2021) · Science Robotics · doi:10.1126/scirobotics.abf1628 · Cited by 102
Machine learning for micro- and nanorobots
Yang, L., Jiang, J., Ji, F., Li, Y., Yung, K. L., Ferreira, A., & Zhang, L. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00859-x · Cited by 110
“Traditional robot swarms have deterministic behaviours driven by individual actuation/control 41 , but swarm behaviours become much more complex at micro-and nanoscales.”
Light-Driven Hovering of a Magnetic Microswarm in Fluid
Ji, F., Jin, D., Wang, B., & Zhang, L. (2020) · ACS Nano · doi:10.1021/acsnano.0c01464 · Cited by 99
Multi-Robot Assembly Strategies and Metrics
Marvel, J. A., Bostelman, R. V., & Falco, J. (2018) · ACM Computing Surveys · doi:10.1145/3150225 · Cited by 98
“…communications may either be explicit in that messages of intent are directly conveyed between robots with or without goals defined a priori (e.g., Rubenstein et al. (2014)), or implicit in that the robots must infer intent by means of sensor-based observations given a shared goal (e.g., Tambe…”
BackgroundStatistics-Based Automated Control for a Swarm of Paramagnetic Nanoparticles in 2-D Space
Yang, L., Yu, J., & Zhang, L. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2946724 · Cited by 98
“be individually actuated [20], [22] and at millimeter scale that distributed actuator networks are applicable to be implemented for individual robot control [21], robots at microscale can hardly”
BackgroundDomino Reaction Encoded Heterogeneous Colloidal Microswarm with On‐Demand Morphological Adaptability
Jin, D., Yuan, K., Du, X., Wang, Q., Wang, S., & Zhang, L. (2021) · Advanced Materials · doi:10.1002/adma.202100070 · Cited by 100
Increasingly Intelligent Micromachines
Huang, T., Gu, H., & Nelson, B. J. (2022) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-042920-013322 · Cited by 100
Stem cell bioengineering: building from stem cell biology
Tewary, M., Shakiba, N., & Zandstra, P. W. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0040-z · Cited by 97
Micro/Nanorobotic Swarms: From Fundamentals to Functionalities
Law, J., Yu, J., Tang, W., Gong, Z., Wang, X., & Sun, Y. (2023) · ACS Nano · doi:10.1021/acsnano.2c11733 · Cited by 97
A robot made of robots: Emergent transport and control of a smarticle ensemble
Savoie, W., Berrueta, T. A., Jackson, Z., Pervan, A., Warkentin, R., Li, S., Murphey, T. D., Wiesenfeld, K. A., & Goldman, D. I. (2019) · Science Robotics · doi:10.1126/scirobotics.aax4316 · Cited by 102
A Modular Dielectric Elastomer Actuator to Drive Miniature Autonomous Underwater Vehicles
Berlinger, F., Duduta, M., Gloria, H., Clarke, D. R., Nagpal, R., & Wood, R. J. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461217 · Cited by 94
“robotics and swarms of untethered robots [8].”
Self-citationMorphological Evolution of Physical Robots through Model-Free Phenotype Development
Brodbeck, L., Hauser, S., & Fumiya Iida, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128444 · Cited by 93
Autonomous task sequencing in a robot swarm
Garattoni, L., & Birattari, M. (2018) · Science Robotics · doi:10.1126/scirobotics.aat0430 · Cited by 90
“We could adopt locally unique IDs, which have been successfully demonstrated with a swarm of 1000 robots (9).”
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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