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Robotic Mission Simulation Tool

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX13CA25P
Agency Tracking Number: 125254
Amount: $124,987.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: H6.01
Solicitation Number: N/A
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-05-23
Award End Date (Contract End Date): 2013-11-23
Small Business Information
Cambridge, MA 02138-4946
United States
DUNS: 007341493
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 James English
 Principal Investigator
 (888) 547-4100
Business Contact
 James Bacon
Title: CFO
Phone: (888) 547-4100
Research Institution

Energid Technologies proposes a software tool to predict robotic mission performance and support supervision of robotic missions even when environments and situations are profoundly unknown. It transcends common Monte Carlo simulations by supporting input parameters for which probability distributions are not available. Stochastic optimization is combined with randomized simulation to bound statistical measures of performance and convey the parameters giving the extreme scenarios. It also provides 3D immersive presentation of those scenarios. The act of performing multiple simulation runs in real time is enabled by the fast simulation capability provided by Energid Technologies' existing software combined with the development of new algorithms and software. The new algorithms cover path planning, scene rendering, sensor modeling, and robot-terrain interaction modeling. In the new software, automatic path planning is calculated using a combination of static and dynamic techniques. Scene rendering for sensor modeling is implemented using fast ray tracing for low-update-rate sensors and ray-tracing-validated rasterization for fast-update-rate sensors. Robot-terrain interaction is calculated through particle simulations implemented on graphics cards. For maximum performance, the new software allows distribution of randomized simulation runs over multiple networked PCs and cloud-based clusters. This combination of fast algorithms and statistical optimization offers a tool that provides new engineering insights and data. The software will be demonstrated on the example mission of searching for ice near near the southern lunar pole, giving evidence of the ability of the tool to support challenging relevant missions.

* Information listed above is at the time of submission. *

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