On Motion Control and Machine Learning for Robotic Assembly

TL;DR

This paper explores methods combining motion control and machine learning to reduce programming effort and improve adaptability of industrial robots in assembly tasks, making robotic automation more accessible and flexible.

Contribution

It introduces new approaches that decrease engineering time and enable robots to better handle unforeseen events in assembly, enhancing usability and efficiency.

Findings

Reduced programming time for robotic assembly

Enhanced robot adaptability to unforeseen events

Increased accessibility for non-engineers

Abstract

Industrial robots typically require very structured and predictable working environments, and explicit programming, in order to perform well. Therefore, expensive and time-consuming engineering work is a major obstruction when mediating tasks to robots. This thesis presents methods that decrease the amount of engineering work required for robot programming, and increase the ability of robots to handle unforeseen events. This has two main benefits: Firstly, the programming can be done faster, and secondly, it becomes accessible to users without engineering experience. Even though these methods could be used for various types of robot applications, this thesis is focused on robotic assembly tasks.