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Visual Relative Navigation via Intelligent Ephemeral Relationships (VRNIER)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W911NF19C0030
Agency Tracking Number: D18C-006-0072
Amount: $225,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: ST18C-006
Solicitation Number: 18.C
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-28
Award End Date (Contract End Date): 2020-01-27
Small Business Information
6800 Cortona Drive
Goleta, CA 93117
United States
DUNS: 054672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Timothy E Fair
 Senior Analyst
 (805) 968-6787
Business Contact
 SBIR Coordinator
Phone: (805) 968-6787
Research Institution
 California Institute of Technology
 Montel Rudolph Montel Rudolph
1200 E. California Blvd. MC 273-6
Pasadena, CA 91125
United States

 (626) 395-4450
 Nonprofit College or University

As unmanned aircraft systems (UAS) become more prevalent there is an increasing desire to automate UAS navigation and control. To enable future UASs to perform a wider variety of missions, the they must be able to complete autonomous relative navigation to accomplish missions. Current technologies rely heavily on GPS measurements, which are undesirable since GPS signals may be unavailable in many DoD applications. Active sensing technologies are also undesirable due to increased SWAP and the desire to limit emitting/communications to enable covert operations. Therefore, a system is needed to provide visual relative navigation (VRN). Toyon and CalTech propose developing a Visual Relative Navigation via Intelligent Ephemeral Relationships (VRNIER) system that consists of passive optical sensors and the processing needed to process the images into accurate six degree of freedom relative estimates. The approach combines computer vision and deep learning for estimating relative platform relationships with online integrity modeling to enable automated VRN. All of the key algorithms will be developed and demonstrated with simulated and surrogate data in Phase I to create a low risk path for demonstrating a prototype system in Phase II. The work leverages Toyon’s extensive history in automated image processing for navigation and other applications.

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

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