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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.24209 |
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| _version_ | 1866918039286448128 |
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| author | Nam, Sanghyeon Kim, Dongmin Choi, Seung-Hwan Kim, Chang-Hyun Kwon, Hyoeun Kawamoto, Hiroaki Lee, Suwoong |
| author_facet | Nam, Sanghyeon Kim, Dongmin Choi, Seung-Hwan Kim, Chang-Hyun Kwon, Hyoeun Kawamoto, Hiroaki Lee, Suwoong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safety-Aware Robust Model Predictive Control for Robotic Arms in Dynamic Environments Nam, Sanghyeon Kim, Dongmin Choi, Seung-Hwan Kim, Chang-Hyun Kwon, Hyoeun Kawamoto, Hiroaki Lee, Suwoong Robotics 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. |
| title | Safety-Aware Robust Model Predictive Control for Robotic Arms in Dynamic Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.24209 |