Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction

Gold T, Völz A, Graichen K (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Pages Range: 7397-7402

Event location: Las Vegas, NV, USA US

DOI: 10.1109/iros45743.2020.9341168

Abstract

The development of modern sensitive lightweight robots allows the use of robot arms in numerous new scenarios. Especially in applications where interaction between the robot and an object is desired, e.g. in assembly, conventional purely position-controlled robots fail. Former research has focused, among others, on control methods that center on robot-environment interaction. However, these methods often consider only separate scenarios, as for example a pure force control scenario.
The present paper aims to address this drawback and proposes a control framework for robot-environment interaction that allows a wide range of possible interaction types.
At the same time, the approach can be used for setpoint generation of position-controlled robot arms, where no interaction takes place. Thus, switching between different controller types for specific interaction kinds is not necessary.
This versatility is achieved by a model predictive control-based framework which allows trajectory following control of joint or end-effector position as well as of forces for compliant or rigid robot-environment interactions.
For this purpose, the robot motion is predicted by an approximated dynamic model and the force behavior by an interaction model.
The characteristics of the approach are discussed on the basis of two scenarios on a lightweight robot.

Authors with CRIS profile

Tobias Gold Chair of Automatic Control Andreas Völz Chair of Automatic Control Knut Graichen Chair of Automatic Control

Related research project(s)

Compliance for a robotic assistance system April 1, 2019 - Sept. 30, 2022

How to cite

APA:

Gold, T., Völz, A., & Graichen, K. (2020). Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7397-7402). Las Vegas, NV, USA, US.

MLA:

Gold, Tobias, Andreas Völz, and Knut Graichen. "Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction." Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA 2020. 7397-7402.

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