SBIR/STTR Research Award
Fast Augmented Reality on the Edge (FARE)
Award amount
$116,500Program and phase
SBIR · Phase IAward year
2020Award details
- Awarding agency
- Department of Defense
- Branch
- Army
- Recipient company
- FASTVDO LLC
- Proposal award date
- 2020-05-27
- Contract end date
- 2020-12-14
- UEI
- K48CQNUZLF83
- Solicitation number
- 19.3
- Topic code
- A19-139
Research abstract
Modeling, simulation and training (MS&T) is the backbone of the military’s ability to train the next generation of warfighters. The US Army is developing the Synthetic Training Environment (STE) for Force 2025 to “provide a cognitive, collective, multi-echelon training and mission rehearsal capability,” at the point of need. This is meant as a software solution to integrate with COTS/GOTS hardware for fully immersive training. Fundamentally, it leverages the latest AR/VR technologies for interactive training of unprecedented realism, i.e., bloodless combat virtually in-situ. While there are many aspects to this challenge, a central technical hurdle is to create the capability of streaming AR/VR content to a potentially dense number of participants (tens to thousands) that is as realistic as possible. The aim is to create a networked application with instant response. Its achievement relies critically on fast communications and proximal computing.
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- H.265 Video Encoding Analysis
- H.265 Video Encoding Analysis
- Compressive Sensing in the Tactical Underwater Environment
- Distributed Co-operative Automatic Target Recognition (ATR) Using Multiple Low Resolution Sensors
- Sub-Pixel Super-Resolution ATR
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- Data compression for low-latency, high-fidelity, and ultra-reliable AR/VR in bandwidth constrained environments.
- Data compression for low-latency, high-fidelity, and ultra-reliable AR/VR in bandwidth constrained environments
- Compressed Parallel Architecture for AR/VR Training in Data Constrained Terrain (COMPACT)