ENLIV-N: Effective Natural Language Interface for Unmanned Vehicle Navigation

Award Information
Agency:
Department of Defense
Amount:
$69,984.00
Program:
SBIR
Contract:
N00014-10-M-0401
Solitcitation Year:
2010
Solicitation Number:
2010.2
Branch:
Navy
Award Year:
2011
Phase:
Phase I
Agency Tracking Number:
N102-177-0621
Solicitation Topic Code:
N102-177
Small Business Information
Scientific Systems Company, Inc
500 West Cummings Park - Ste 3000, Woburn, MA, -
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
Y
Duns:
859244204
Principal Investigator
 Kai-yuh Hsiao
 Senior Research Engineer
 (781) 933-5355
 kai-yuh.hsiao@ssci.com
Business Contact
 Jay Miselis
Title: Corporate Controller
Phone: (781) 933-5355
Email: contracts@ssci.com
Research Institution
 Stub
Abstract
Scientific Systems Company, Inc. (SSCI), in collaboration with Spatio Systems LLC and Prof. Bennett at Olin College, proposes to design and implement ENLIV-N, an Effective Natural Language Interface for Vehicle Navigation. This multi-modal user interface will translate natural language directions and gesture commands into vehicle waypoints, and coordinate feedback to the operator via video display and synthesized voice. Language understanding will be provided by Spatio's DUNE system, which uses corpora of real-world directions to train a novel phrase-based parsing approach that is uniquely robust to disfluencies. DUNE automatically augments its vocabulary using tagged databases (currently Flickr), and has been tested successfully on two ground robots and an aerial robot in indoor environments. Dr. Hsiao at SSCI will leverage his Ph.D. dissertation work on language-integrated robotic planning systems to provide multimodal integration and planning for the ENLIV-N system. Olin's vehicle from the DARPA Urban Challenge will serve as the autonomous test platform. Phase I work will focus on adapting DUNE in simulation to a mission of operational relevance, while designing the gesture, vehicle, and planning elements. At the end of Phase II, we will demonstrate our approach by commanding a real-world autonomous ground vehicle with waypoint navigation and obstacle avoidance.

* information listed above is at the time of submission.

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