| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
|
| Initial Amendment Date: | August 17, 2020 |
| Latest Amendment Date: | October 13, 2020 |
| Award Number: | 2024670 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jordan Berg
jberg@nsf.gov (703)292-5365 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | January 1, 2021 |
| End Date: | December 31, 2023 (Estimated) |
| Total Intended Award Amount: | $31,867.00 |
| Total Awarded Amount to Date: | $31,867.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
150 MUNSON ST NEW HAVEN CT US 06511-3572 (203)785-4689 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
9 Hillhouse Ave New Haven CT US 06520-8286 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | NRI-National Robotics Initiati |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
The goal of this National Robotics Initiative (NRI) project is to enable mixed teams of humans and robots to work together to accomplish physically demanding object manipulation tasks in complex environments. For this project, soft robots are exclusively considered, because traditional robots are too heavy and potentially dangerous to work closely with people. Humans can effectively work together to move a bulky, heavy object because they are able to use their understanding of group goals and individual capabilities to interpret physical cues and quickly infer each other's intention. Thus, the first step in extending this ability to robots is to understand how groups of people recognize and react to pushing and pulling from other team members. The project also emphasizes the necessity of managing uncertainty when working with soft robots and with people -- soft robots because they deform significantly under typical task loads, and people because their movements may be difficult for robots to predict. Potential applications of the research can range from expediting logistics and material handling, to improving human safety in dangerous and/or hard-to-reach environments such as mining, oil rigs, logging, and search and rescue. To this end, a collaboration with a local search and rescue team will solicit feedback on human-robot co-manipulation throughout the project. Underrepresented undergraduate students will be trained with a STEM education tool leveraging soft robotics, and the students will then work to disseminate this training to local K-12 classrooms.
Co-manipulation can be defined as the actions taken and the signals sent by many collaborating agents while moving a single large object. This research will enable co-manipulation between humans and robots, and is focused on the following three main thrusts: 1) modeling, controlling, and planning effective stiffness trajectories for soft robots to deal with task uncertainty, 2) quantifying and modeling human intention and consensus during manipulation, and 3) developing algorithms that incorporate intention, consensus, and uncertainty to execute co-manipulation tasks. Building on prior work on model predictive control algorithms for large-degree-of-freedom soft robots, stiffness trajectories will be generated as part of the soft robot control based on estimates of task uncertainty. Trials with human collaborators moving large objects in real life and in virtual reality will allow the development of algorithms that predict consensus and motion of the group. Finally, given a reasonable estimate of the short-term motion goal of a group, the resulting algorithms will also generate robot motion and stiffness trajectories to help a group reach consensus more efficiently by reducing uncertainty. This research will pioneer the novel combination of natural physical interaction, control for safe robots, multi-agent coordination, and planning/acting in a distributed manner.
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.
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.
The objective of this project was to introduce a shortened version of an 8-hour design curriculum developed with prior NSF funding. Through a five-year study, we investigated whether participation in a soft robot design experience would improve students' perceptions of engineering compared to a more traditional robot design experience. Our study found evidence that the soft robotics unit improved students' motivation to pursue STEM careers and tinkering self-efficacy. With a shortened version of the curriculum, we planned to hold annual workshops to broaden student access to the proven benefits of soft robot design experiences.
Due to the pandemic, our initial workshop plans shifted to an online format, piloted in August 2021, which proved beneficial for broader dissemination without travel. The online workshop included pre-fabricated materials mailed to participants, with 15 modular mold kits made and rigorous testing of virtual instruction. Participants included two BYU students, 11 UML students, and two teachers from AMSA Charter School. Post-workshop, CAD files and instructions were shared for further dissemination, and feedback was collected to improve subsequent workshops. The online pilot in 2021 led to in-person workshops in 2022, with the first at Yale attracting 13 students, mostly female and URMs. A second in-person workshop followed, with improvements in mold design and handout procedures. Further workshops in 2023 for high school students showed progress in manufacturing processes and instructions, with the 27 student participants rating the workshops highly. Facilitator and student manuals were standardized for easy adoption, and pre- and post-surveys were implemented for longitudinal progress tracking. A 1-hour version of the workshop has been rigorously documented and sustainably integrated into Yale's Pathways to Science program.
Last Modified: 05/14/2024
Modified by: Rebecca Kramer-Bottiglio
Please report errors in award information by writing to: awardsearch@nsf.gov.
