Safety-Aware Nonlinear Model Predictive Control for Physical Human-Robot Interaction

Artemiy Oleinikov, Sanzhar Kusdavletov, Almas Shintemirov, Matteo Rubagotti

Research output: Contribution to journalArticlepeer-review

Abstract

This letter proposes a nonlinear model predictive control (NMPC) approach for real-time planning of point-to-point motions of serial robot manipulators that share their workspace with a human. The NMPC law solves a nonlinear program online, based on a kinematic model, and guarantees safety by constraining the robot speed within the time-varying bounds determined by the speed-and-separation-monitoring (SSM) principle. Closed-loop stability is proven in detail, and the performance (in terms of productivity) of the proposed method is tested against standard SSM schemes via experiments on a Kinova Gen3 robot.

Original languageEnglish
Article number9440695
Pages (from-to)5665-5672
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number3
DOIs
Publication statusPublished - Jul 2021

Keywords

  • human-aware motion planning
  • nonlinear model predictive control
  • Optimization and optimal control
  • physical human-robot interaction
  • speed and separation monitoring

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Safety-Aware Nonlinear Model Predictive Control for Physical Human-Robot Interaction'. Together they form a unique fingerprint.

Cite this