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1 - STORMY: Storm-Aware USV Operations using Multi-Objective Autonomy

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-F-0097
Agency Tracking Number: N193-A02-0131
Amount: $149,818.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N193-A02
Solicitation Number: 19.3
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-21
Award End Date (Contract End Date): 2020-04-20
Small Business Information
500 West Cummings Park, Suite 3000, Woburn, MA, 01801
DUNS: 859244204
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dr. Donald Eickstedt Dr. Donald Eickstedt
 Lead Research Engineer
 (781) 933-5355
 deickstedt@ssci.com
Business Contact
 Lora Loyall
Phone: (781) 933-5355
Email: contracts@ssci.com
Research Institution
N/A
Abstract
STORMY (Storm-Aware USV Operations using Multi-Objective Autonomy) will allow a USV to weigh global mission objectives such as arrivals at a time-prioritized sequence of locations against damage to the vessel due to high seas or collision with other vessels during long-duration missions. The proposed WAVES technology addresses these issues with a two-tiered approach to vehicle motion which considers global route planning and plan execution as two distinct processes. Although missions proceed in the context of the global plan using the environmental forecast, local wind, current and sea state conditions are monitored using tracking filters and a Parametric Bayesian Estimator that attempts to estimate where parameters of the current environmental conditions are in relation to the forecast and adjusts the forecast as necessary. The environmental parameters estimated with the Bayesian Estimator are kept in several simple classes (e.g. “high sea states delayed 3 hours”) since more sophisticated weather forecasting is not possible onboard the USV in real-time. This approach allows WAVES to trade off forecast-based global route plans designed to meet overall mission objectives with the maneuvering needed to meet unexpected real-time local conditions due to forecasting error.

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

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