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

Artemiy Oleinikov, Sanzhar Kusdavletov, Almas Shintemirov, Matteo Rubagotti

2021

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DOI 10.1109/lra.2021.3083581
Data confidence 85 high
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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.