The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
Programmable self-assembly in a thousand-robot swarm
Abstract
Large-scale robotic self-assembly: When individuals swarm, they must somehow communicate to direct collective motion. Swarms of robots need to deal with outliers, such as robots that move more slowly than the rest. Rubenstein et al. created a large swarm of programmed robots that can form collaborations using only local information. The robots could communicate only with nearby members, within about three times their diameter. They were able to assemble into complex preprogrammed shapes. If the robots' formation hit snags when they bumped into one another or because of an outlier, additional algorithms guided them to rectify their collective movements. Science , this issue p. 795
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ResultThe Politics of Robot Autonomy
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“In the near future, we plan to implement the consensus achievement scenario presented in this paper on a real swarm of 100 Kilobots by leveraging the potential of a 2 m2 Kilogrid system [36].”
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Kim, J., Chung, H., Lee, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07414 · Cited by 7
“[31] propose a flock of programmable self-assembly robots, which can create a target shape without external intervention.”
BackgroundMethod(2014) · Nature · doi:10.1038/516311a · Cited by 7
Qu, Y., Xin, B., Wang, Q., et al. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3346277 · Cited by 6
“As a classical problem about swarm robots, the shape formation [14] can be applied to various fields such as self-assembly [15], [16], [17] and information relay [18].”
BackgroundSelf-Healing Distributed Swarm Formation Control Using Image Moments
Liu, C. L., Ridgley, I. L. D., Elwin, M. L., et al. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3401171 · Cited by 6
“Inotherwords,theintendedformationshould berepresentableusingasmallnumberofvariables,independent ofthenumberofrobots.”
Self-citationPlane odd elasticity problems: Failure of Saint-Venant’s principle
Lai, A., Zhou, J., Fu, G., et al. (2025) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2025.113459 · Cited by 6
Distributed spatial awareness for robot swarms
Jones, S. P., & Hauert, S. (2025) · Autonomous Robots · doi:10.1007/s10514-025-10228-1 · Cited by 6
“Rubenstein et al. [24] used kilobots [25] to build arbitrary 1-connected shapes by individual robots extending a seed formation and localising themselves against already positioned static robots, likewise Li et al. [26] progressively construct a shape with new agents positioning themselves against…”
Adaptive Path Formation in Self-Assembling Robot Swarms by Tree-Like Vascular Morphogenesis
Soorati, M. D., Zahadat, P., Ghofrani, J., et al. (2019) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-05816-6_21 · Cited by 6
Mean-Shift Shape Formation of Multi-Robot Systems Without Target Assignment
Zhang, Y., Zhou, R., Li, X., et al. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3349926 · Cited by 6
“Another strategy focuses on removing the process of target assignment, that is, assignment-free approaches [20], [21], [22], [23], [24], [25], [26].”
BackgroundMethodInfluentialEfficient Energy Management for Intelligent Microrobotic Swarms: Design and Impact
Zhu, M., McCaskill, J. S., Karnaushenko, D., Bandari, V. K., Gemming, S., Kanoun, O., Arnold, M. G., & Schmidt, O. G. (2024) · Advanced Energy Materials · doi:10.1002/aenm.202400881 · Cited by 6
Safety Flocking of Networked Lagrangian Systems With Event-Triggered Communication
Luo, X., Fu, Y., Wang, J., & Li, X. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3442441 · Cited by 6
“Flocking is a common collective movement behavior in nature, which can be observed in flocks of birds, schools of fish, etc. [12], [13], [14].”
BackgroundIsmail, S., Mokhtar, N. B., & Ahmad, H. (2024) · Accounting Research Journal · doi:10.1108/arj-01-2024-0033 · Cited by 6
Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning
Liu, H.-S., Kuroki, S., Kozuno, T., Sun, W.-F., & Lee, C. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801665 · Cited by 7
“These include generating intermediate goal coordinates [2], [21], [23], computing gradient fields based on the shape [18]–[20], identifying the saliency maps of images [24], and employing other strategies [25], [26].”
Structure, motion, and multiscale search of traveling networks
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Synchronized dispersion of robotic swarms using XP colonies
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A distributed self-assembly approach for hollow shape in swarm robotics
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“In nature, group of individuals can self-assemble into a wide variety of forms, purely through local interactions and simple rules [6].”
BackgroundRobotic Specialization in Autonomous Robotic Structural Assembly
Bernus, B., Trinh, G., Gregg, C., et al. (2020) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero47225.2020.9172620 · Cited by 6
“These teams can range from homogeneous swarm robots [8] to heterogeneous robot systems with specialized robots for each task [9], they can also have centralized or decentralized control [10].”
BackgroundSwarm and Collective Capabilities for Multipotent Robot Ensembles
Kosak, O., Bohn, F., Eing, L., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61470-6_31 · Cited by 6
“…the particle swarm optimization algorithm [27], the commonly known flocking behavior originally analyzed in [21], shaping and formation algorithms [22], and distribution algorithms [15,16] make use of the same set of local actions: measuring one or multiple specific parameters, communicating with…”
VMCS: Elaborating APF-Based Swarm Intelligence for Mission-Oriented Multi-UV Control
Park, S., Kim, H. T., & Kim, H. (2020) · IEEE Access · doi:10.1109/access.2020.3044055 · Cited by 6
“However, in the real world, aligned flocking or shaping an image on the sky [1] (or ground [2]) have been demonstrated only by proactive path-planning, which shows the limitation of multi-UV’s empirical application.”
BackgroundActive Modular Environment for Robot Navigation
Kameyama, S., Okumura, K., Tamura, Y., & Défago, X. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561111 · Cited by 6
“Johnson and Mitra [22] studied a theoretical model of distributed traffic control where a fixed environment consists of cells that guide mobile entities from predetermined sources to targets.”
BackgroundVerification of Acoustic-Wave-Oriented Simple State Estimation and Application to Swarm Navigation
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Ji, X., Zhang, W., Chen, S., et al. (2021) · Electronics · doi:10.3390/electronics10202547 · Cited by 6
Meta-control of social learning strategiesPreprint
Yaman, A., Bredèche, N., Çaylak, O., Leibo, J. Z., & Lee, S. W. (2021) · arXiv (Cornell University) · doi:10.1371/journal.pcbi.1009882
“This may prove to be important in real-world applications such as the internet of things and swarm robotics [66, 2, 40, 51, 14].”
BackgroundSelf‐Organization in Network Sociotechnical Systems
Maltseva, S., Kornilov, V., Барахнин, В. Б., & Gorbunov, A. (2022) · Complexity · doi:10.1155/2022/5714395 · Cited by 6
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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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