Context

UR strives to improve the quality of their cobots by utilizing cutting-edge technologies such as machine learning/AI. UR is currently investigating if machine learning can be applied for TCP stabilization. After finishing a motion in a given pose, the time to stabilize the TCP position affects the cycle time, where sharp cycle times are among the most important performance indicators delivered by robot manufacturers.

Challenge 

Develop a real-time capable machine learning solution for TCP stabilization of a physical UR robot transporting a variety of payloads (known and unknown) which can positively impact cycle times for poses across the workspace.

To get the team started, consider the following:

• Can solutions exploit data from encoders and TCP accelerometer?
• Can solutions exploit data from force/torque sensor to estimate payload mass and inertia?

Keywords: Control optimization, velocity profile, acceleration profile

Tools, methods and materials

The challenge can be addressed with any machine learning tool allowing for generic time series data as input. The selection of tools and methods is left to the team.

From UR, the team will receive an UR robot arm and documentation on how to command user-defined motions and how to access data from the encoders, accelerometer and force/torque sensor. In addition, UR will be available to discuss details of the challenge along the way.