Study uses VR to speed training workers using robots
Researchers from the University of Georgia have developed a new virtual reality (VR) space to help humans working with robots train more efficiently and effectively.
Working with robots is becoming increasingly common in the recycling industry, aiding in task automation and simplifying complex work, such as disassembly. This is crucial for recovering parts and valuable materials from electronics that are nearing the end of their useful life. However, disassembly tasks often pose their own challenges and VR can help train workers in this industry.
“Unlike assembly, which has a very standard procedure, disassembly is slightly more complicated,” said Beiwen Li, corresponding author of the study and an associate professor in UGA’s College of Engineering. “It may not work out the best if we just inverse the whole assembly procedure.”
The researchers developed VR Co-Lab to help reduce confusion. Training employees digitally enables them to practice disassembling recyclables without damaging materials and to learn how to avoid injury and collisions with a robot.
In the study, users practised taking apart a hard disk with robotic help in a virtual space. The virtual workstation is similar to the real workstation on the job, including the various tools and machinery needed for disassembly, as well as the robot helper.
During training, workers followed a step-by-step procedure for disassembling the hard disk. Human users handled more precise tasks, such as unscrewing or picking and placing small bolts, while the robot arm managed larger bolts and loose items. The program also provided feedback to the user, measuring the time it took to complete the session and the number of mistakes made.
“There are a lot of tasks. It requires a complicated training for workers, typically,” Li said. “So, if we have a VR system, that will be very helpful in shortening training time. It is much easier than having pages and pages of written documents to be read by the user.”
VR Co-Lab utilises the Meta Quest Pro, which uses its cameras to track upper body movements in the wrists, elbows, shoulders, and torso. This enables the program to plan the robot’s movements based on the user’s actions, preventing collisions and enhancing interactivity between the human and the robot.
The programme warns of potential hazards that could lead to injury, such as collisions with the robotic arm, while teaching users how to avoid them. The system can also be used to determine how quickly the robotic arm can operate without overwhelming the employee.
Li and his team are planning more comprehensive user testing in the future to ensure that the system is useful for a range of skill levels and applicable to tasks beyond disassembling hard disks. Enhancing the training process will be important as the use of robots becomes increasingly common.
“Robots are going to be important for the future of the recycling industry because they can do a lot of disassembly steps automatically. That can help reduce the labour shortage,” Li said. “Because disassembly is so complicated, it involves a human to work together with a robot. And that’s basically our motivation for developing this VR system for training.”
The study was published in Machines and funded by a grant from the National Science Foundation. Yashwanth Maddipatla of Iowa State University and Sibo Tian, Xiao Liang, and Minghui Zheng of Texas A&M University co-authored the study.
https://doi.org/10.3390/machines13030239
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