Computer Vision based intelligent Assistant for Mistake Proofing of Complex Maintenance Tasks on Navy Ships
Abstract
The maintenance service is an important task for the Navy. The Navy is responsible for managing maintenance on all its ships throughout each ship’s service life. All ships have an expected service life in the range of 40 to 50 years; all ships require maintenance throughout their service lives. Maintenance costs can reach 50 percent of the entire product lifecycle (from design to disposal) and are expected to increase over the next quarter century as more complex Navy ships are commissioned in the field. Navy ship maintenance encounters many challenges including but not limited to (a) unanticipated work requirements, (b) remote and hard to access equipment location, (c) workforce inexperience, and (d) workload fluctuations. The purpose of this Naval Engineering Education Consortium (NEEC) proposed project is to investigate advanced deep learning based computer vision methods and algorithms to enable nextgeneration Handheld Augmented Reality (HAR) based complex maintenance task applications. The proposed project also provides an excellent opportunity to engage graduate and undergraduate students in a Navy related research problem and expose them to open-ended, hands-on projects that are more effective in promoting experiential learning. The research focus will encourage students to consider exciting career opportunities associated with the US Naval Enterprise. The key objectives of this proposal is to develop (a) advanced deep learning based computer vision methods and algorithms to enable next-generation Handheld Augmented Reality (HAR) based complex maintenance tasks, (b) new software pipelines that enable ease of reconfiguration of native HAR applications, and (c) small-scale lab and Navy maintenance applications to demonstrate the efficacy of the developed computational pipeline. The proposed project will also develop a comprehensive engineering educational program and outreach activities that span graduate and undergraduate students, and the general public.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 30, 2020
- Source ID
- N001741910025
Entities
People
- Rahul Rai
Organizations
- United States Navy