Abstract
Assembly tasks executed by a robot have been studied broadly. Robot assembly applications in industry are achievable by a well-structured environment, where the parts to be assembled are located in the working space by fixtures. Recent changes in manufacturing requirements, due to unpredictable demanded products, push the factories to seek new smart solutions that can autonomously recover from failure conditions. In this way, new dual arm robot systems have been studied to design and explore applications based on its dexterity. It promises the possibility to get rid of fixtures in assembly tasks, but using less fixtures increases the uncertainty on the location of the components in the working space. It also increases the possibility of collisions during the assembly sequence. Under these considerations, adding perception such as force/torque sensors have been done to produce useful data to perform control actions. Unfortunately, the interaction forces between mating parts produced non-linear behavior. Consequently, machine learning algorithms have been considered an alternative tool to avoid the non-linearity. In this work we introduce an assembly strategy for an industrial dual arm robot based on the combination of a discrete event controller and Deep Neural Networks (DNN) to solve the peg-in-hole assembly. Our results show the difference between the use of DNN with one and with two force/torque sensors during the assembly task and demonstrate a 30% increase in the assembly success ratio when using a double force/torque sensor.
Highlights
Due to rapid changes in production demand and to product changes, new challenges have arisen in the manufacturing of products, a trend that has increased throughout the years and has affected most of the industry
This paper presents a novel approach to investigate the peg-in-hole assembly with a dual arm robot
The robotic testbed is composed of an industrial robot assembly cell that includes a dual arm robot SDA20D with two 7 DOF articulated arms and 1 rotating base giving a total of 15 DOF as a whole system Figure 5
Summary
Due to rapid changes in production demand and to product changes, new challenges have arisen in the manufacturing of products, a trend that has increased throughout the years and has affected most of the industry. More robots that work in an unstructured environment for assembly tasks will be required in factories, where fixtureless operations could be executed, giving high flexibility to the production processes [1]. Considering that current automation systems do not support intelligent solutions for assembly tasks, a great opportunity arises, that is, to develop practical methodologies to include machine learning algorithms in assembly problems with robots. When non-desired contact among objects occurs, the assembly cycle requires assistance from an operator. In automated cells, when such conditions appear, the robot is programmed to reject the assembled components and start a new cycle. This represents a delay in the production schedule and additional costs due to scrap generation
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