| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | August 21, 2019 |
| Latest Amendment Date: | August 21, 2019 |
| Award Number: | 1932187 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Bruce Kramer
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2019 |
| End Date: | August 31, 2024 (Estimated) |
| Total Intended Award Amount: | $1,199,956.00 |
| Total Awarded Amount to Date: | $1,199,956.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
70 WASHINGTON SQ S NEW YORK NY US 10012-1019 (212)998-2121 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
New York NY US 10012-1019 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CPS-Cyber-Physical Systems |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Aging civil infrastructure is a critical worldwide problem that affects daily life, making it important to innovate more efficient and economical repair and construction methods for civil structures. Additive manufacturing, or 3D printing, offers a promising way to fulfill this compelling need. However, almost all current additive manufacturing methods rely on gantry-based systems that can only build structures within rigid frames, thereby restricting printing speed and scale, thus hindering their use in maintenance and construction. This award supports fundamental research to establish collective additive manufacturing, a novel robotics-based approach for large-scale 3D printing. Collective additive manufacturing uses a team of autonomous mobile robots to jointly print large-scale 3D structures. The results of the research will have a potentially wide range of applications in civil infrastructure maintenance and construction, to post-disaster response and extraterrestrial construction. The project is based on a convergent research approach involving robotics, artificial intelligence, control theory, and dynamical systems, which culminates in formal and informal learning activities to broaden participation of underrepresented groups in engineering.
Collective additive manufacturing envisions the use of teams of mobile robots to overcome key limitations of existing gantry-based additive manufacturing, including its small scale and slow printing speed. To unleash the full potential of collective additive manufacturing, several scientific boundaries must be pushed, ensuring optimal deployment of multiple mobile robots that print large structures according to an engineered, virtual design. This research will fill critical knowledge gaps in robotic localization, control, and coordination, to realize a robotic team that intentionally and actively modifies its surroundings to successfully complete its printing task. This interdisciplinary research program will unfold along three thrusts: artificial intelligence for planning and localization, model predictive control to adapt to printing disturbances and substrate variations, and distributed control to elicit stable collective dynamics. Theoretical advancements will proceed alongside with experimental research toward demonstrating the potential of collective additive manufacturing to accurately and efficiently print large structures in real-world settings.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Summary of Major Activities
This project focused on advancing mobile 3D printing technology, with the aim of enabling efficient, autonomous construction through the use of mobile robots. Key areas of research included mobile robot manipulation, localization, control systems, and coordination of multiple robots for large-scale construction tasks. The work also emphasized the development of swarm robotic systems, model-predictive control (MPC), and novel algorithms for navigation, multi-agent coordination, and mobile additive manufacturing.
Key Achievements
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Mobile 3D Printing, Object Manipulation, and Navigation
A major advancement of the project was the development of a theoretical framework and a computational system for autonomous mobile manipulation and construction. The SNAC framework (Simultaneous Navigation and Construction) and the Mobile Object Rearrangement (MOR) system aim to enable robots to navigate, manipulate objects, and construct structures without the need for GPS or high-precision localization. The MOR systems use first-person images to estimate object poses and uncertainties, enabling robots to rearrange objects in complex environments. It could also incorporate decentralized control strategies for swarm robotics, allowing multiple robots to collaborate on large-scale construction tasks with minimal communication. These innovations provided theoretical foundations for real-world applications of mobile 3D printing, such as construction, where dynamic and unstructured environments are common.
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Mobile 3D Printing System Development
Two mobile 3D printing prototypes were developed as part of the project. One utilized a modified TurtleBot for precise localization, while the other incorporated advanced control systems for improved accuracy. These prototypes laid the foundation for autonomous, large-scale printing in construction, demonstrating the feasibility of mobile robots in building complex structures.
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Swarm Robotics and Multi-Agent Coordination
A mixed-reality testing platform for swarm robotics was developed, allowing for the efficient testing of multi-robot systems in controlled environments. By exploring decentralized control strategies, the project demonstrated how robots can work collaboratively in large groups to perform tasks such as 3D printing. These findings offer promising applications for industries such as construction and manufacturing, where large numbers of robots can collaborate to perform complex tasks.
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Nonlinear Model Predictive Control (MPC)
The research team made significant strides in improving MPC, a key control method for ensuring precise robot actions in dynamic environments. The integration of force feedback for tasks like sanding and construction enhanced real-time obstacle avoidance and physical interaction control. Additionally, the team advanced the use of infinite horizon value functions, ensuring safer and more reliable robot performance in unstructured settings.
Broader Impacts
The advancements made through this project hold transformative potential for industries such as construction, manufacturing, and urban planning. The development of autonomous, collaborative robots capable of working in challenging or dangerous environments can significantly reduce human risk and increase efficiency in construction and infrastructure maintenance. Furthermore, the project lays the groundwork for the integration of AI and robotics into urban environments, potentially revolutionizing how buildings and infrastructure are designed, constructed, and maintained.
The project's focus on swarm robotics and decentralized control also has far-reaching implications for tasks such as disaster relief, environmental monitoring, and large-scale infrastructure projects. These robotic systems can operate with minimal human intervention, creating safer and more scalable solutions for pressing global challenges.
Training and Professional Development
Throughout the project, several PhD students, postdoctoral fellows, and undergraduate researchers were trained in advanced robotics, AI, and control theory. The project provided hands-on experience in building and testing robotic systems, preparing the next generation of engineers and researchers for careers in these fields. The open-source tools and frameworks developed also contributed to the broader robotics community, enabling further advancements in mobile robotics and construction automation.
Future Directions
The project team plans to deploy mobile 3D printing systems in real-world environments to validate and refine the algorithms developed. Future work will extend SNAC/MOR to handle larger-scale, unstructured environments, addressing dynamic obstacles and more complex construction tasks. The team also aims to expand swarm robotics for larger multi-robot teams, pushing the boundaries of real-time coordination in large-scale infrastructure projects.
In conclusion, the achievements of this project represent a significant leap forward in autonomous systems and their application to construction and urban environments, promising a future where robots can autonomously and efficiently build infrastructure, reducing costs, risks, and environmental impacts.
Last Modified: 12/30/2024
Modified by: Chen Feng
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