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arXiv · 2505.24209

Safety-Aware Robust Model Predictive Control for Robotic Arms in Dynamic Environments

Sanghyeon Nam·Dongmin Kim·Seung-Hwan Choi·Chang-Hyun Kim·Hyoeun Kwon·Hiroaki Kawamoto·Suwoong Lee
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Abstract

Robotic manipulators are essential for precise industrial pick-and-place operations, yet planning collision-free trajectories in dynamic environments remains challenging due to uncertainties such as sensor noise and time-varying delays. Conventional control methods often fail under these conditions, motivating the development of Robust MPC (RMPC) strategies with constraint tightening. In this paper, we propose a novel RMPC framework that integrates phase-based nominal control with a robust safety mode, allowing smooth transitions between safe and nominal operations. Our approach dynamically adjusts constraints based on real-time predictions of moving obstacles\textemdash whether human, robot, or other dynamic objects\textemdash thus ensuring continuous, collision-free operation. Simulation studies demonstrate that our controller improves both motion naturalness and safety, achieving faster task completion than conventional methods.

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Sanghyeon Nam, Dongmin Kim, Seung-Hwan Choi, Chang-Hyun Kim, Hyoeun Kwon, Hiroaki Kawamoto, Suwoong Lee. 2025-05-30. Safety-Aware Robust Model Predictive Control for Robotic Arms in Dynamic Environments. https://arxiv.org/abs/2505.24209

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