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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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Tan, M., Tian, P. W., Zhang, Q., Zhu, G., Liu, Y., Cheng, M., & Shi, F. (2022) · Nature Communications · doi:10.1038/s41467-022-32892-y · Cited by 34
Sensor coverage robot swarms using local sensing without metric information
Ramaithitima, R., Whitzer, M., Bhattacharya, S., et al. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139670 · Cited by 33
“cal communication-based algorithms for robot swarms have been used to construct various shapes [9] in an environment.”
MethodDevice and programming abstractions for spatiotemporal control of active micro-particle swarms
Lam, A. T., Samuel-Gama, K. G., Griffin, J., et al. (2017) · Lab on a Chip · doi:10.1039/c7lc00131b · Cited by 33
Variable structure robot control systems: The RAPP approach
Zieliński, C., Stefańczyk, M., Kornuta, T., Figat, M., Dudek, W., Szynkiewicz, W., Kasprzak, W., Figat, J., Szlenk, M., Winiarski, T., et al. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.05.002 · Cited by 33
“one thousand robots filling-in a predefined 2D space [35]; two boxpushing robots perceiving the environment to accomplish a task [36]; a robot using a motion capture system as a remote sensor, guiding a formation of robots which observe through their own sensors the relative distance to the leader, which moves along a…”
BackgroundInfluence of sensorial delay on clustering and swarming
Piwowarczyk, R., Selin, M., Ihle, T., et al. (2019) · Physical review. E · doi:10.1103/physreve.100.012607 · Cited by 33
Kosak, O., Wanninger, C., Hoffmann, A., Ponsar, H., & Reif, W. (2018) · Sensors · doi:10.3390/s19010017 · Cited by 35
“Homogeneous approaches [50,51] are well-established in the swarm robotics community, where robots all have the same, often very limited, abilities [132].”
BackgroundCrowdHMT: Crowd Intelligence With the Deep Fusion of Human, Machine, and IoT
Guo, B., Liu, Y., Liu, S., Yu, Z., & Zhou, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3194726 · Cited by 34
“Some researchers [68] are inspired by multicellular organisms and complex animal structures (e.”
BackgroundAchievements and future directions in self‐reconfigurable modular robotic systems
Dokuyucu, H. İ., & Özmen, N. G. (2022) · Journal of Field Robotics · doi:10.1002/rob.22139 · Cited by 36
“Such structures (Østergaard et al., 2006; Spröwitz et al., 2010), applying a sandbox approach (Thalamy et al., 2021, 2022) and million modules march (Kirby et al., 2007; Rubenstein et al., 2014) modules and on developing distributed controller algorithms.”
BackgroundMethodInfluentialPattern phase transitions of self-propelled particles: gases, crystals, liquids, and mills
Cheng, Z., Chen, Z., Vicsek, T., Chen, D., & Zhang, H. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/10/103005 · Cited by 33
Wei, H., Mao, Q., Guan, Y., et al. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.048 · Cited by 33
Multi-target trapping with swarm robots based on pattern formation
Zhang, S., Liu, M., Lei, X., Huang, Y., & Zhang, F. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.04.008 · Cited by 33
Simulating Kilobots Within ARGoS: Models and Experimental Validation
Pinciroli, C., Talamali, M. S., Reina, A., Marshall, J. A. R., & Trianni, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00533-7_14 · Cited by 33
“One robotic platform currently having momentum in swarm robotics is the Kilobot [20,22].”
MethodAdoni, W. Y. H., Fareedh, J. S., Lorenz, S., et al. (2024) · Drones · doi:10.3390/drones8100575 · Cited by 32
Lee, S. K., Fekete, S. P., & McLurkin, J. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915624974 · Cited by 32
“Other exciting recent work on formations of massive swarms of robots is presented by Rubenstein et al. (2014), who describe how to achieve geometric shapes with simple robots.”
BackgroundMulti-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions
Guerrero, J., Oliver, G., & Valero, Ó. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170659 · Cited by 32
Distributed Control for Spatial Self-Organization of Multi-agent Swarms
Krishnan, V., & Martı́nez, S. (2018) · SIAM Journal on Control and Optimization · doi:10.1137/16m1080926 · Cited by 34
“In the context of robotic swarms, programmable self-assembly of two-dimensional shapes with a thousand-robot swarm is demonstrated in [31].”
BackgroundSynthetic development: learning to program multicellular self-organization
Toda, S., Brunger, J. M., & Lim, W. A. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.02.008 · Cited by 29
Motility versus fluctuations in mixtures of self-motile and passive agents
Hinz, D. F., Panchenko, A., Kim, T.-Y., et al. (2014) · Soft Matter · doi:10.1039/c4sm01562b · Cited by 32
Self-organized sorting limits behavioral variability in swarms
Copenhagen, K., Quint, D. A., & Gopinathan, A. (2016) · Scientific Reports · doi:10.1038/srep31808 · Cited by 32
Recent trends in robot learning and evolution for swarm robotics
Kuckling, J. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1134841 · Cited by 37
“Thanks to these properties, swarm robotics is considered a prominent approach to control large groups of autonomous robots (Rubenstein et al., 2014; Werfel et al., 2014; Mathews et al., 2017; Garattoni and Birattari, 2018; Slavkov et al., 2018; Yu et al., 2018; Li et al., 2019; Xie et al., 2019; Dorigo et al., 2020) a…”
BackgroundLily: A miniature floating robotic platform for programmable stochastic self-assembly
Haghighat, B., Droz, E., & Martinoli, A. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139452 · Cited by 31
Pruitt, J. N., & Pinter‐Wollman, N. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.1766 · Cited by 33
“Such collective traits have captured the imagination of scientists including ecologists [2,3], behaviourists [4,5], mathematicians [6] and engineers [7,8], perhaps, more than anything else, because these collective traits are thought to emerge without central control [9].”
BackgroundKhaluf, Y., Pinciroli, C., Valentini, G., & Hamann, H. (2017) · Swarm Intelligence · doi:10.1007/s11721-017-0137-6 · Cited by 31
“Other examples of behavioral features that influence the system’s scalability and that crucially depend on the swarm density are waiting times in object manipulation [20] or aggregation [35], as well as scaling of gradient values for localization in self-assembly [33].”
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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