NSF award 2541312 · Mississippi State University

CAREER: An AI-Driven Simulation Framework for Human Autonomy Planning, Adaptive Control, and Immersive Virtual Reality Training

Final report due

29 December 2031

in 1,898 days · The award ends on 31 August 2031.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension3 May 2031in 1658 days
award ending 2031-08-31 · Research.gov
This one is ours, not NSF’s.
Extension request17 Jul 2031in 1733 days
award ending 2031-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2031in 1768 days
award ending 2031-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2031in 1898 days
award ending 2031-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2031in 1898 days
award ending 2031-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2031in 1898 days
award ending 2031-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

NSF lists this award's end date as 31 August 2031.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2031.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2541312
Period of support: 1 Sep 2026 – 31 Aug 2031
Amount: $516,081
Awardee: Mississippi State University, Mississippi State, MS
Program: CAREER: FACULTY EARLY CAR DEV, M3X - Mind, Machine, and Motor
Directorate: ENG

Abstract

Teams of humans and robots can make search-and-rescue operations safer and more effective, especially in dangerous environments such as structural fires, where poor visibility and rapidly changing hazards pose risks to first responders and civilians. However, current approaches often treat robot sensing, risk assessment, decision support, and emergency-response training as separate activities, making it difficult to…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.