Friction Identification in a Pneumatic Gripper

• Published in: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 9948 - 9953
• Main Authors: Romeo, Rocco A., Maggiali, Marco, Pucci, Daniele, Fiorio, Luca
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24-10-2020
• Subjects: Data models; Dynamics; Force; Friction; Grippers; Load modeling; Predictive models
• ISSN: 2153-0866
• DOI: 10.1109/IROS45743.2020.9341593

Mechanical systems are typically composed of a number of contacting surfaces that move against each other. Such surfaces are subject to friction forces. These dissipate part of the actuation energy and cause an undesired effect on the overall system functioning. Therefore, a suitable model of friction is needed to elide its action. The choice of such a model is not always straightforward, as it is influenced by the system properties and dynamics. In this paper, we show the identification of different friction models and evaluate their prediction capability on an experimental dataset. Despite being state-of-the-art models, some modifications were introduced to improve their performance. A pneumatic gripper was used to collect the data for the models evaluation. Two experimental setups were mounted to execute the experiments: information from two pressure sensors, a load cell and a position sensor was employed for the identification. During the experiments, the gripper was actuated at different constant velocities. Results indicate that all the identified models offer a proper prediction of the real friction force.