Refined Post-Impact Velocity Prediction for Torque-Controlled Flexible-Joint Robots

Camilo Andres Rey Arias, Wouter Weekers, Marco Morganti, Vincent Padois, Alessandro Saccon (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Predicting the post-impact velocity for torque-controlled flexible-joint robots enhances impact-aware control schemes which exploit intentional collisions for achieving dynamic robotic manipulation and locomotion. Compared to a previous approach based on a fully rigid-robot assumption, this paper shows how an improvement in the post-impact velocity prediction can be obtained by taking into account the joints' motor inertias, transmission ratios, and low-level torque control gains, as well as the impact surface friction. The paper also proposes a more robust method to estimate the gross post-impact velocity profile from experimental data via a polynomial fit. The improvement of the new post-impact velocity prediction is illustrated by means of both numerical simulations as well as 50 experimental trials on a commercially available torque-controlled robot. The recorded impact data and prediction algorithms are shared openly for reproducibility and further research.
Original languageEnglish
Article number10430226
Pages (from-to)3267-3274
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume9
Issue number4
Early online date9 Feb 2024
DOIs
Publication statusPublished - 1 Apr 2024

Keywords

  • Robots
  • Predictive models
  • Collision avoidance
  • Numerical models
  • Robot sensing systems
  • Mathematical models
  • Friction
  • dynamics
  • Contact modeling
  • force control

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