Collaborative Mobile Manufacturing in Uncertain Scenarios

Project description:

Large-scale manufacturing is common practice in several industries: transportation, maritime, energy, aerospace and construction. Finishing operations, such as trimming, sanding and painting, are necessary to guarantee tolerances and required properties are met. For humans, those tasks are stressful, and may lead to physical discomfort and potential for repetitive strain injury. To improve safety for human workers and the quality of work for finishing operations on large-scale parts, a team of autonomous mobile robots, assisted by human supervisors, can be deployed. The objective of this project is to design a distributed intelligent architecture for teams of mobile robots to perform finishing operations under human supervision. The project has three aspects: development of perception capabilities for robotic system, the design of localization and mapping architectures for multi-robot systems, and the creation of robust control structures. The project has been funded by the National Science Foundation.

Team members:

Azmyin Kemal, PhD Student in Mechanical Engineering, LSU, email: akamal4@lsu.edu, Personal website: https://www.linkedin.com/in/azmyinkamal/
Donovan Gegg, Undergraduate Student in Mechanical Engineering (part of the Accelerated Master at LSU), email: dgegg1@lsu.edu
Jude Rodgers, Undergraduate Student in Mechanical Engineering, email:jroge76@lsu.edu

Previous team mebers:

Joshua Nguyen (MS in Mechanical Engineering, LSU 2022)
James Oubre (BS in Electrical Engineering, LSU 2022)
Manuel Bailey (BS in Mechanical Engineering, LSU 2022)

Publications:

[1] J. Nguyen, M. Bailey, I. Carlucho, and C. Barbalata, “Robotic manipulators performing smart sanding operation: A vibration approach,” in 2022 International Conference on Robotics and Automation (ICRA), IEEE, 2022, pp. 2958–2964.
[2] J. Oubre, W. Ard, J. Nguyen, and C. Barbalata, “Towards a fully autonomous robotic system for
detection and removal of surface defects in fiber glass panels,” in 6th IFAC International Conference
on Intelligent Control and Automation Sciences, 2022.
[3] Ding, J. Ye, C. Barbalata, J. Oubre, C. Lemoine, J. Agostinho, and P. Genevieve, “Next-generation
perception system for automated defects detection in composite laminates via polarized computational
imaging,” in The Composites and Advance Materials Expo, 2021.

Website:

https://nri-cmmus-lsu.github.io/dist/index.html

Media:

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