Elena R. Messina, John A. Horst, Thomas R. Kramer, Hui-Min Huang, Tsung-Ming Tsai, E Amatucci
We are building an inspection workstation development environment to use as a testbed for understanding what types of knowledge, e.g., data, algorithms, and processes, can increase the productivity of inspection operations. Inspection can be more efficient through reducing the need for fixturing, integrating the generation of process plans and their execution within the controller, and reducing the errors or data losses that occur by translating the models to different formats. Initial configuration of inspection systems can be less costly through the use of open architectures that are constructed from components. Key elements of our work include in situ feature-based planning, vision-driven part pose estimation, and software methods to facilitate construction of manufacturing controllers. These provide a rich environment in which to study the categories of knowledge that are useful in intelligent control of inspection workstations. This paper describes our vision, approach, and preliminary results.
Proceedings of Intelligence in Automation & Robotics Symp.
, Horst, J.
, Kramer, T.
, Huang, H.
, Tsai, T.
and Amatucci, E.
A Knowledge-Based Inspection Workstation, Proceedings of Intelligence in Automation & Robotics Symp., Bethesda, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=820644
(Accessed August 1, 2021)