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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Doing Business As Name:Massachusetts Institute of Technology
PD/PI:
  • Daniela Rus
  • (617) 258-7567
  • rus@csail.mit.edu
Co-PD(s)/co-PI(s):
  • Mark Yim
  • Hod Lipson
  • Eric Klavins
Award Date:08/27/2007
Estimated Total Award Amount: $ 2,000,000
Funds Obligated to Date: $ 2,000,000
  • FY 2007=$2,000,000
Start Date:01/01/2008
End Date:12/31/2012
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:490100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:EFRI-ARESCI:Controlling the Autonomously Reconfiguring Factory
Federal Award ID Number:0735953
DUNS ID:001425594
Parent DUNS ID:001425594
Program:EFRI Research Projects
Program Officer:
  • Radhakisan Baheti
  • (703) 292-8339
  • rbaheti@nsf.gov

Awardee Location

Street:77 MASSACHUSETTS AVE
City:Cambridge
State:MA
ZIP:02139-4301
County:Cambridge
Country:US
Awardee Cong. District:07

Primary Place of Performance

Organization Name:Massachusetts Institute of Technology
Street:77 MASSACHUSETTS AVE
City:Cambridge
State:MA
ZIP:02139-4301
County:Cambridge
Country:US
Cong. District:07

Abstract at Time of Award

PI: Daniela Rus Institution: Massachusetts Institute of Technology, University of Washington, Cornell University, University of Pennsylvania Proposal Number: 0735953 EFRI-ARESCI: Controlling the Autonomously Reconfiguring Factory This project, with investigators from the Massachusetts Institute of Technology, Cornell University, the University of Pennsylvania, and the University of Washington, seeks to establish new fundamental theories for understanding autonomously reconfigurable systems under conditions of uncertainty. Natural systems possess the remarkable ability to create deterministic structures and processes out of a huge variety of raw materials. They have extreme robustness with respect to the source of raw materials and high adaptability with respect to their behaviors, due in part to their stochastic nature. These properties are also desirable for engineered systems such as automated factories, cooperative robotic systems, and networked computational systems. However, currently the design and assembly of these systems relies on deterministic processes and supply chains, which makes them fragile with respect to fluctuations in supply and limited in their ability for structural reconfiguration and functional adaptation. The goal of this project is to explore, and physically demonstrate, a novel paradigm for robust construction and adaptive reconfiguration of physical systems from elementary components, under uncertainty and variability of material resources. The investigators envision a manufacturing process where the source and target are defined indirectly, and the path between them is subject to stochastic fluctuations requiring strategic decisions. The project addresses (1) the theoretical foundations of reconfiguring systems by examining distributed algorithms, control theory, and statistical physics approaches to modeling system behavior; (2) methods for analysis and synthesis by analyzing the information flow in these systems and the development of a synthetic design methodology; and (3) experimental validation by using the investigator's existing and new platforms to demonstrate construction and swarming tasks. The goal of the proposed system is to be built on-the-fly and instantiated at a disaster site to provide support by creating physical structures and facilitating information flow for first responders. The system can also be instantiated in the context of construction and fabrication, bringing manufacturing processes to new levels of customization and robustness and automation. This study can lead to a better understanding of biological systems, which are self-organizing at many different levels. Finally, the proposed approaches to engineering and analyzing stochastic adaptive reconfiguring machines may generate hypotheses for neuroscientists, psychologists and biologists regarding the organizational and algorithmic nature of adaptation and robustness in complex systems.

Publications Produced as a Result of this Research

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Mac Schwager, Daniela Rus, and Jean-Jacques Slotine "Decentralized Adaptive Coverage Control for Netowrked Robots" International Journal of Robotics Research, v.28, 2009, p.357.

Garcia, Ricardo Franco Mendoza and Hiller, Jonathan D and Stoy, Kasper and Lipson, Hod "A Vacuum-Based Bonding Mechanism for Modular Robotics" IEEE Transactions on Robotics, v.27, 2011, p.878.

Gilpin, K., Kotay, K., Vasilescu, I., and D. Rus "Miche: Modular Shape Formation by Disassembly" International Journal of Robotics Research, v.27, 2008, p.345.

N. Napp and E. Klavins "A Compositional Framework for Programming Stochastically Interacting Robots" Inernational Journal of Robotics Research, v.30, 2011, p.713.

K. Gilpin and D. Rus "Shape Shifting Robots: from Self-Assembly to Self-Disassembly" IEEE Robotics and Automation Magazine, v.17, 2010, p.38.

S. Yun and D. Rus "Optimal Self-assembly of Modular Manipulators with Active and Passive Modules" Autonomous Robots, v.31, 2011, p.181.

Park, M., Chitta, S., Teichman, A., and Yim, M "Automatic Configuration Recognition Methods in Modular Robots" International Journal of Robotics Research, v.27, 2008, p.403.

Tolley, Michael T and Kalontarov, Michael and Neubert, Jonas and Erickson, David and Lipson, Hod "Stochastic Modular Robotic Systems: A Study of Fluidic Assembly Strategies" IEEE Transactions on Robotics, v.26, 2010, p.518.

M. Yim "Planetary Contingency: A Competition Educating Graduate Students in Reconfigurable Robotics" IEEE Robotics & Automation Magazine, v.15, 2008, p.14.

Tolley, MT and Lipson, H "On-line assembly planning for stochastically reconfigurable systems" International Journal of Robotics Research, v.30, 2011, p.1566.

Michael Tolley and Hod Lipson "On-line assembly planning for stochastically reconfigurable systems" International Journal of Robotics Research, v.30, 2011, p.1566.

K. Gilpin and D. Rus "Shape Shifting Robots: from Self-Assembly to Self-Disassembly" IEEE Robotics and Automation Magazine, v.17, 2010, p.38.

N. Napp, S. Burden, and E. Klavins "Setpoint Regulation for Stochastically Interacting Robots" Autonomous Robots, v.30, 2010, p.57.

N. Correll and D. Rus "Peer-to-Peer Learning in Robotics Education: Learning from a Challenge Class" ASEE Computers in Education Journal, v.1, 2010, p.69.

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