SBIR-STTR Award

Team AVATAR is prepared to design, develop and prove the feasibility of an innovative Simplified Intelligent Augmented Reality for Quality Assurance (SIA QA) prototype to provide the government (GOVT) with a hardware agnostic, marker less (location based) tracking AR system with enhanced Field of View (FOV) and camera fidelity necessary for aircraft-installed wiring systems, integrated with an AI/ML engine and advanced database architecture to identify, visualize, compare, and indicate wiring harness accuracy. SIA QA will learn proper wiring harness installation, herein called Should-be and compare it to what our AR records from V-22 Engine Nacelles, herein called As-is wiring. SIA QA will enable users to quickly and accurately determine aircraft wiring harnesses routing properness, beginning with the V-22 with the potential to expand across all NAVAIR aircraft types.

Benefit:
SIA QA can have an immediate positive impact on aircraft readiness, since the QA aspect can apply to other systems beyond aircraft wiring such as fuel, hydraulic and environmental control/bleed air systems. A summary of SIA QA benefits are: Non-Proprietary, Open Source, S1000D format for all Media: Government not tied to specific contractor for updates Increased Aircraft Readiness: Almost 100% of V-22 Osprey have wiring issues Human factor-based design hands-free access to information (Headset only) Human error reduced to near zero: Critical errors lead to significant mechanical failures Reduced maintenance time and cost by reducing the need for additional spare parts caused by human error Reduced time on QA and Inspection Tasks Information at the Point of Need - Improves system knowledge by having the correct configuration available at the inspection site Organic sustainment of AR Content using SimplifyXR.

Keywords:
Database Integration, Database Integration, Hardware agnostic, Augmented Reality, Markerless Tracking, quality assurance, Artificial Intelligence/Machine Learning

In Phase II, Team AVATAR will provide a tangible, resilient, and validated prototype solution to the original topic resulting in a working solution that leverages AI and AR to resolve critical issues in wiring harness inspection & maintenance. SIA QA will empower maintainers & inspectors to wear an AR headset, approach a specific V-22 wiring harness, and get both contextually relevant information about the status & state of the wires, as well as on-the-job support. SIA QA integrates two components: (1) an offline process & technology that captures Should-be data and makes it exploitable by (2) an online process & technology that quickly compares, in real-time, Should-be data to As-is data, and acts upon the comparison, identifying improper wiring harness routing & wire type identification for exposed bundles, correcting the wiring harness, and to refine underlying databases & offline process. The instantiation of SIA QA will focus on augmenting Phase I concepts for offline and online processes. Instantiation of Offline processes will include capturing aircraft data (should travel restrictions permit local visits) by photographing, videographing, and LIDAR capture of correctly-wired harnesses for critical V-22 systems. The maturation of SIA QA will target both the Offline & Online processes. We will expand the depth, breadth, & type of coverage of the Should-Be database: respectively, this expansion will target higher granularity of system levels being included in the database (depth), higher variability in conditions under which data is captured such as lighting, angles, etc. (breadth), and additional methods of collections such as video and LIDAR (coverage). The leveraging of wiring schematics will be an exploratory goal in Phase II. We believe that schematics provide significant additional data for SIA QA to run its AI engine and support maintainers & inspectors. The hardware upgrades will be a trade study -like objective for Phase II. With the increasing availability and deployment of new hardware supporting computer vision and AR, Team AVATAR needs to ensure that SIA QA works on the equipment and hardware most likely to be employed by maintenance crews. The affordance of process improvement reasoning is another technical objective in Phase II. Recognizing the nature of the ongoing support SIA QA will provide to inspectors & technicians alike, there are insights to be inferred from the repeated and widespread usage of SIA QA. The evaluation of SIA QA will also cover both types of processes. On the Offline side, we will measure the data coverage of our Should-Be database against the larger needs for a full platform (quantitative assessment) as well as the soundness of the data itself using standard practices (qualitative assessment). We will review the architecting of SIA QA to ensure its resilience against future needs (RMF and on-ramping to government systems). On the Online side, we will perform a principled dual assessment of SIA QA.

Benefit:
Non-Proprietary, Open Source, S1000D format for all Media Reduced labor burden on expert inspectors and maintainers as novices will be able to perform as near experts. Increased Readiness (Example, currently 53% USN & USMC F/A-18 aircraft are unfit to fly due to maintenance backlog) Human factor-based design hands-free access (with AR Headset) Reduced maintenance time and costs by reducing the need for additional spare parts caused by human error Reduced time on task Reduced maintenance cost due to less rework Resolve hardware shortage issues (limited number of IETM laptops) Information at the Point of Need - improves memory retention and reduces errors Organic sustainment of training content and updates of technical directives

Keywords:
Artificial Intelligence, Augmented Reality, Advanced Computer Vision, Aircraft Wiring, quality assurance, Machine Learning, Data Tagging, SIA QA