| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
|
| Initial Amendment Date: | August 29, 2023 |
| Latest Amendment Date: | August 29, 2023 |
| Award Number: | 2301707 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Siddiq Qidwai
sqidwai@nsf.gov (703)292-2211 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2023 |
| End Date: | August 31, 2025 (Estimated) |
| Total Intended Award Amount: | $196,993.00 |
| Total Awarded Amount to Date: | $196,993.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
1 LMU DR LOS ANGELES CA US 90045-2650 (310)338-4599 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
1 LMU DR UHALL STE 4900 LOS ANGELES CA US 90045-2650 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
FRR-Foundationl Rsrch Robotics, ERI-Eng. Research Initiation |
| 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, 47.070 |
ABSTRACT
Unmanned aerial vehicles, or drones, have successfully been used to monitor ground activity. However, using small drones for extended periods of time is not yet possible, thus limiting their implementation. For instance, small quadcopters that can be easily transported and deployed do not exceed forty minutes of flying time in most cases and are susceptible to unexpected failure such as damage from natural hazards. On the other hand, robust quadcopters capable of longer flying times have larger dimensions and weight that prohibit ease of deployment. As an alternative to a single robust drone, this Engineering Research Initiation (ERI) award will support fundamental research to enable a network of small drones to monitor ground activity with the goal of uninterrupted operation and fault tolerance by sharing information with one another including knowledge of targets detected. A demonstration of this concept will be made through wildfire monitoring in collaboration with the US Forest Service. This award will sustain research at a predominantly undergraduate institution. Both undergraduate and graduate students will participate in the research effort.
The monitoring problem under consideration in this research is related to the well-known multiple traveling salesman problem and its variants, namely Vehicle Routing Problem with Time Window and Multiple Depot Drone Routing Problem. However, the solution to these routing problems cannot be used as-is because they would consider drone tours that visit each cluster (region) only once, not periodically. Moreover, they do not consider fault tolerance. This research aims at a fault-tolerant solution that (1) characterizes target clusters via distributed estimation of Gaussian Mixture Models, (2) coordinates flight formations and search paths using game theory to avoid central control, and (3) performs point set registration of adjacent images from drones to increase accuracy of target locations. The research will not only promote progress in wildfire monitoring but also for any other natural or human activity that can be characterized with Gaussian Mixture Models.
This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.
