DOI
https://doi.org/10.25772/EJRG-S139
Defense Date
2023
Document Type
Thesis
Degree Name
Master of Science
Department
Electrical & Computer Engineering
First Advisor
Patrick Martin, Ph.D.
Abstract
While traditional manufacturing production cells consist of a fixed base robot repetitively performing tasks, the Industry 5.0 flexible manufacturing cell (FMC) aims to bring Autonomous Industrial Mobile Manipulators (AIMMs) to the factory floor. Composed of a wheeled base and a robot arm, these collaborative robots (cobots) operate alongside people while autonomously performing tasks at different workstations. AIMMs have been tested in real production systems, but the development of the control algorithms necessary for automating a robot that is a combination of two cobots remains an open challenge before the large scale adoption of this technology occurs in industry. Currently popular docking based methods require a mount point for the docking station and considerable time for the robot to locate and park. These limitations necessitate the consideration and implementation of more modern robot control and path planning techniques. This work proposes and implements a simulation testbed that uses a contemporary whole-body control, OCS2, to perform more flexible pick-and-place tasks. Within this testbed, an Industry 5.0 based pick-and-place framework is deployed, fine-tuned and tested. This system supports the one-shot lead-through based assignment of a prepick position by an operator, thus enabling the cobot to drive to this position and successfully pick up the part agnostic of base orientation and/or position. The proposed system allows robot path planning experimentation and assessment against a variety of cost and constraint values, and is capable of being modified to support various vision based part locating algorithms.
Rights
© 2023 Richard Ethan Hollingsworth
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
4-23-2023