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

Artemiy Oleinikov; Sanzhar Kusdavletov; Almas Shintemirov; Matteo Rubagotti

doi:10.1109/LRA.2021.3083581

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

Artemiy Oleinikov, Sanzhar Kusdavletov, Almas Shintemirov, Matteo Rubagotti

Research output: Contribution to journal › Article › peer-review

19 Citations (Scopus)

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 language	English
Article number	9440695
Pages (from-to)	5665-5672
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	6
Issue number	3
DOIs	https://doi.org/10.1109/LRA.2021.3083581
Publication status	Published - 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

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10.1109/LRA.2021.3083581

Cite this

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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.",

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AB - 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.

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

Abstract

Keywords

ASJC Scopus subject areas

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