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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/2507.04640 |
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| _version_ | 1866908437352284160 |
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| author | Origane, Yuki Hoischen, Nicolas Huang, Tzu-Yuan Kurabayashi, Daisuke Sosnowski, Stefan Hirche, Sandra |
| author_facet | Origane, Yuki Hoischen, Nicolas Huang, Tzu-Yuan Kurabayashi, Daisuke Sosnowski, Stefan Hirche, Sandra |
| contents | This paper focuses on the trajectory optimization of an underwater suspended robotic system comprising an uncrewed surface vessel (USV) and an uncrewed underwater vehicle (UUV) for autonomous litter collection. The key challenge lies in the significant uncertainty in drag and weight parameters introduced by the collected litter. We propose a dynamical model for the coupled UUV-USV system in the primary plane of motion and a risk-aware optimization approach incorporating parameter uncertainty and noise to ensure safe interactions with the environment. A stochastic optimization problem is solved using a conditional value-at-risk framework. Simulations demonstrate that our approach reduces collision risks and energy consumption, highlighting its reliability compared to existing control methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk-Aware Trajectory Optimization and Control for an Underwater Suspended Robotic System Origane, Yuki Hoischen, Nicolas Huang, Tzu-Yuan Kurabayashi, Daisuke Sosnowski, Stefan Hirche, Sandra Systems and Control This paper focuses on the trajectory optimization of an underwater suspended robotic system comprising an uncrewed surface vessel (USV) and an uncrewed underwater vehicle (UUV) for autonomous litter collection. The key challenge lies in the significant uncertainty in drag and weight parameters introduced by the collected litter. We propose a dynamical model for the coupled UUV-USV system in the primary plane of motion and a risk-aware optimization approach incorporating parameter uncertainty and noise to ensure safe interactions with the environment. A stochastic optimization problem is solved using a conditional value-at-risk framework. Simulations demonstrate that our approach reduces collision risks and energy consumption, highlighting its reliability compared to existing control methods. |
| title | Risk-Aware Trajectory Optimization and Control for an Underwater Suspended Robotic System |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.04640 |