Towards an Augmented Reality-based Cyber-Physical
Production System Planner
Name
mueller-drmuelr-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
2.3 MB
Format
Adobe PDF
Checksum (MD5)
71481787887a89a9f3ed661b601be399
Author(s)
Mueller, David
Advisor(s)
Davis, Randall
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Investment in automation by small and medium-sized enterprise (SME) manufacturers in the United States has lagged behind their larger counterparts for decades, despite comprising a majority of the nation’s manufacturing industry. The cyber-physical production systems (CPPSs) introduced by Industry 4.0 promise to bolster productivity and efficiency, but only for those enterprises which invest in constituent technologies. These technologies are not easily integrated in existing factories, typically requiring installation of invasive infrastructure and continuous technical support. Robotic integration is typically performed by specialized third-party firms or by in-house staff with extensive technical training, such as engineers. SME manufacturers are particularly sensitive to the complexities of robot integration due to limited access to technologists, and their need for frequent reconfiguration under economies of scope. This thesis introduces Marve: the Mobile Augmented Reality Visual Editor. Marve is a proof-of-concept Android application that enables line workers to directly configure and control an autonomous mobile robot (AMR)-backed hybrid intralogistics system using lowcost consumer hardware. Workers can use Marve’s augmented reality (AR)-based interface to define and visualize the essential geometry and components of such a system. Once configured, workers are able to simulate how the system would respond to their requests to move material throughout the factory. The use of AR enables extensive work to be done at the planning stage of CPPS integration by line workers themselves, bypassing the need for modeling by engineers. Marve relies exclusively on fiducials and visual-inertial odometry (VIO) for localization, and fiducial tags for object tracking, thus eliminating the need for supporting infrastructure. Taken together, these features make Marve an easy on-ramp for SMEs seeking to transition legacy production lines into the CPPSs of Industry 4.0.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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