On Motion Control and Machine Learning for Robotic Assembly
This addresses the need for more flexible and user-friendly robot programming in industrial settings, but appears incremental as it builds on existing motion control and machine learning approaches.
The thesis tackles the problem of reducing engineering work and increasing adaptability in robotic assembly by presenting methods that speed up programming and make it accessible to non-experts, though specific numerical results are not provided.
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.