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EX-SCAN: Autonomous Inspection System for Aircraft Surface Coatings

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-16-C-0020
Agency Tracking Number: F14A-T09-0185
Amount: $749,922.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF14-AT09
Solicitation Number: 2014.0
Timeline
Solicitation Year: 2014
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-05-03
Award End Date (Contract End Date): 2018-05-14
Small Business Information
15400 Calhoun Drive
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 David Handelman
 (301) 294-4252
 dhandelman@i-a-i.com
Business Contact
 Mark James
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
 Carnegie Mellon University
 Mel Siegel
 
5000 Forbes Avenue Array
Pittsburgh, PA 15213
United States

 (412) 983-2626
 Nonprofit College or University
Abstract

Current methods for inspecting the external surfaces of low-observable (LO) aircraft are time consuming and error prone. Technology that can reduce inspection times and minimize human error will benefit the Air Force by increasing assessment reliability and aircraft availability while reducing maintenance costs. To address this need, Intelligent Automation (IAI) and Carnegie Mellon University (CMU) propose to continue developing EX-SCAN, an autonomous non-contact optical inspection system that can detect, register, and characterize defects in OML surface coatings for LO assessment.The key innovation of EX-SCAN is the integration of a self-localizing mobile robot equipped with an array of cameras and corresponding software to ensure rapid, autonomous, 100% coverage of the aircraft OML.In Phase I, IAI and CMU developed a functional, scale prototype of an inspection robot to scan a scale model aircraft, developed initial image processing algorithms, and conducted a study on the feasibility of scanning a full-size aircraft. In Phase II, the team will mature the system by adding autonomous mobility and navigation, and enhancing the defect processing software. Focus areas will include enhancing the localization technology, expanding defect detection, coordinating defect registration technology with movable cameras and localization software, improving characterization technology, and integrating these capabilities into a system.

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

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