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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:OREGON STATE UNIVERSITY
Doing Business As Name:Oregon State University
PD/PI:
  • Geoffrey A Hollinger
  • (541) 737-4933
  • geoff.hollinger@oregonstate.edu
Award Date:06/15/2021
Estimated Total Award Amount: $ 496,065
Funds Obligated to Date: $ 496,065
  • FY 2021=$496,065
Start Date:09/01/2021
End Date:08/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Adaptable and Robust Multi-Robot Decision Making through Generalized Sequential Stochastic Task Assignment
Federal Award ID Number:2103817
DUNS ID:053599908
Parent DUNS ID:053599908
Program:FRR-Foundationl Rsrch Robotics
Program Officer:
  • Erion Plaku
  • (703) 292-8695
  • eplaku@nsf.gov

Awardee Location

Street:OREGON STATE UNIVERSITY
City:Corvallis
State:OR
ZIP:97331-8507
County:Corvallis
Country:US
Awardee Cong. District:04

Primary Place of Performance

Organization Name:Oregon State University
Street:
City:Corvallis
State:OR
ZIP:97331-2140
County:Corvallis
Country:US
Cong. District:04

Abstract at Time of Award

This project envisions teams of multiple robotic systems cooperatively and autonomously executing complex missions in the physical world. These missions include environmental monitoring, search and rescue, and scientific exploration, where robots are tasked to provide timely information to end users. The success of many robotic missions depends on a small number of critical, irreversible, and high-impact decisions. Examples of such decisions include selecting where to deploy aerial robots from ground robots, determining how to deploy communication hardware, and specifying when to execute specific motion behaviors. In current fielded robotic systems, these types of decisions are largely left to human operators, who typically do not have the required situational awareness, reasoning skills, or available time to make these decisions effectively. This work seeks to bridge the gap between current multi-robot systems that require significant human input to make high-impact decisions and future intelligent robotic systems capable of executing the most effective behaviors at the right time and location. The ultimate objective of this project is to develop new algorithmic solutions for making high-impact decisions in heterogeneous multi-robot teams. When reasoning over such decisions, many variables must be considered, such as the mission goals, available actions, environment belief models, future rewards, and the behaviors and capabilities of other robots. Many of these variables carry a significant degree of uncertainty, have a prior belief of their value, and may change based on dynamic conditions and robot observations. New algorithmic solutions for reasoning over this information will be developed by formulating and solving new generalizations of the sequential stochastic assignment problem (SSAP). These SSAP generalizations require reasoning over the uncertain future values of robot actions, accounting for information acquired in situ, exploiting dependencies in the reward distributions, and computing policies in a decentralized manner. Validation will be performed through both simulated and field experiments for marine monitoring and environment exploration scenarios. The developed algorithms will be made publicly available through open source distribution and will help foster ongoing collaborations with marine and environmental scientists. 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.

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