Engineering Commons^™

Effect Of Automation Level On Cognitive Workload When Collaborating With A Robotic Assistant, Mitchell A. Champagne

LSU Master's Theses

Manufacturing robotics have been used for decades to perform repetitive tasks, or tasks that require increased levels of speed, strength, or precision to meet production and specification requirements. Determining the appropriate degree of automation for both the human and robot collaborative team members is critical to optimize production as well as user experience. If the degree of automation is too high, the human will be out of the loop which can result in the loss of situational awareness and be detrimental to intervention time and accuracy. If the degree of automation is too low, then the human may experience greater …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

Designing For The Future: Sensor And Gauge Assembly Work Cell Design, Kaitryana Leinbach

Honors Theses

No abstract provided.