FabNavi is a support system to capture assembling processes with videos/pictures and replaying data on (remote) tabletop. The system records works on tabletop with an overhead camera, and presents full scale videos/pictures on the tabletop (Figure 1).
Figure 1. The FabNavi system
The FabNavi system proposes visual instruction to assemble physical objects in remote places: (1) Recording the assembly processes easily, (2) Sharing these instruction on the web, (3) Replaying them on the (remote) tabletop (Figure 2).
Figure 2. Basic Concept of the FabNavi system
The system also aims to collect data of FAB processes in the real world, understand them using recognition techniques, and generate “recipes” (semi-) automatically (Figure 3).
Figure 3. Understanding FAB processes for generating recipes.
Implementation
The FabNavi system mainly consists of (1) API server, (2) Capture client, (3) Browser Client.
API Server
Running on Amazon EC2 & S3. Ready for Big data.
Capture Client
iPhone & Android Client.
Browser Client
Electron App both for Win/Mac.
Applications will be delivered on http://fabnavi.org/
Screen capture of fabnavi web app
Project Members
Core
Koji Tsukada (Associate Prof., Future Univ. Hakodate)
Koji Tsukada, Keita Watanabe, Daisuke Akatsuka, and Maho Oki, FabNavi: Support system to assemblephysical objects using visual instructions, Paper presented at Fab10, Barcelona, 2-8 July (2014) [PDF]
Kazuya Nakae and Koji Tsukada. 2018. Support System to Review Manufacturing Workshop through Multiple Videos. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion (IUI’18). Article 4, 2 pages, 2018.[PDF]
中江 一哉，沖 真帆，塚田 浩二，モノづくりワークショップを対象とした振り返り動画作成支援システム，インタラクション2017論文集，インタラクティブ発表（プレミアム発表），pp.499-502, 2-509-34, Mar, 2017．[PDF]
デジタルカメラの小型化/低価格化は著しく,ほとんどの PC /スマートフォン/タブレットに搭載されるなど,コモディティ化が進んでいる.しかし,こうした IoT(Internet of Things)的応用においては,実世界での適切な固定方法,センサと連携した実世界の認識機能,クラウドと連携した適切な認識/フィードバック機能等が必要となり,カメラモジュール自体は安価になっても,それを用いた開発には多くの労力が必要であった.そこで本研究では,実世界の様々な箇所に手軽に固定し,様々なセンサと連動しつつ,クラウドと連携した柔軟な画像処理やフィードバックが行えるカメラベースのデバイスツールキット「AttachCam」を提案する.
