Paper Info
Title
Using Convolutional Neural Networks For Assembly Activity Recognition In Robot Assisted Manual Production
Abstract
Due to ever-shortening product life cycles and multi variant products the demand for flexible production systems that include humanrobot collaboration (HRC) rises. One key factor in HRC is stress that occurs because of the unfamiliar work with the robot. To reduce stress induced strain for assembly tasks we propose an adjustment of cycle times to the human's performance, so that the stress that is exerted on the working person by a waiting robot is minimized. For an autonomous adaptation of the cycle time, the production system should be aware of the human's actions and assembly progress without the need to inform the system manually. Therefore, we propose an activity recognition in assembly based on a machine learning technique. A convolutional neural network is used to distinguish between different activities during the assembly by analyzing motion data of the hands of the working person. The results show that the network is suitable for distinguishing between nine different assembly activities like screwing with a screwdriver, screwing with a hexagon wrench or general assembly and further activities.
Year
DOI
Venue
2018
10.1007/978-3-319-91244-8_31
HUMAN-COMPUTER INTERACTION: INTERACTION IN CONTEXT, HCI INTERNATIONAL 2018, PT II
Keywords
Field
DocType
Human-robot collaboration, Human-machine systems, Manual assembly, Machine learning, Neural networks, Convolutional neural network, Pattern recognition, Activity recognition, Motion tracking
General assembly,Activity recognition,Computer science,Convolutional neural network,Human–computer interaction,Wrench,Artificial intelligence,Robot,Artificial neural network,Match moving
Conference
Volume
ISSN
Citations 
10902
0302-9743
1
PageRank 
References 
Authors
0.35
15
2
Name
Order
Citations
PageRank
Henning Petruck110.69
Alexander Mertens26818.37