AI Enhanced Human-Robot Collaboration in Smart Manufacturing

Author: ORCID icon orcid.org/0000-0001-8691-9795
Yu, Tian, Mechanical and Aerospace Engineering - School of Engineering and Applied Science, University of Virginia
Advisor:
Chang, Qing, EN-Mech & Aero Engr Dept, University of Virginia
Abstract:

In the era of Industry 4.0, smart manufacturing systems have witnessed unprecedented growth, ushering in a new era of productivity and flexibility. The integration of robots into manufacturing environments has been instrumental in achieving these advancements. However, the full potential of human-robot collaboration (HRC) is yet to be fully realized. This dissertation addresses novel approaches to enhance human-robot collaborative efficiency and reduce the burden of reprogramming through the application of reinforcement learning (RL)/ deep reinforcement learning (DRL) based task scheduling and robot learning from demonstration (LfD).

Degree:
PHD (Doctor of Philosophy)
Keywords:
Human-Robot Collaboration, Robot Motion Planning, Reinforcement Learning, Task Scheduling
Language:
English
Rights:
All rights reserved (no additional license for public reuse)
Issued Date:
2023/11/15