ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2502.02443
94
1

A Null Space Compliance Approach for Maintaining Safety and Tracking Performance in Human-Robot Interactions

4 February 2025
Zi-Qi Yang
Miaomiao Wang
Mehrdad R. Kermani
ArXiv (abs)PDFHTML
Abstract

In recent years, the focus on developing robot manipulators has shifted towards prioritizing safety in Human-Robot Interaction (HRI). Impedance control is a typical approach for interaction control in collaboration tasks. However, such a control approach has two main limitations: 1) the end-effector (EE)'s limited compliance to adapt to unknown physical interactions, and 2) inability of the robot body to compliantly adapt to unknown physical interactions. In this work, we present an approach to address these drawbacks. We introduce a modified Cartesian impedance control method combined with a Dynamical System (DS)-based motion generator, aimed at enhancing the interaction capability of the EE without compromising main task tracking performance. This approach enables human coworkers to interact with the EE on-the-fly, e.g. tool changeover, after which the robot compliantly resumes its task. Additionally, combining with a new null space impedance control method enables the robot body to exhibit compliant behaviour in response to interactions, avoiding serious injuries from accidental contact while mitigating the impact on main task tracking performance. Finally, we prove the passivity of the system and validate the proposed approach through comprehensive comparative experiments on a 7 Degree-of-Freedom (DOF) KUKA LWR IV+ robot.

View on arXiv
@article{yang2025_2502.02443,
  title={ A Null Space Compliance Approach for Maintaining Safety and Tracking Performance in Human-Robot Interactions },
  author={ Zi-Qi Yang and Miaomiao Wang and Mehrdad R. Kermani },
  journal={arXiv preprint arXiv:2502.02443},
  year={ 2025 }
}
Comments on this paper