Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911007470780416 |
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| author | Das, Hemjyoti Vu, Minh Nhat Ott, Christian |
| author_facet | Das, Hemjyoti Vu, Minh Nhat Ott, Christian |
| contents | In this work, we present a novel approach to augment a model-based control method with a reinforcement learning (RL) agent and demonstrate a swing-up maneuver with a suspended aerial manipulation platform. These platforms are targeted towards a wide range of applications on construction sites involving cranes, with swing-up maneuvers allowing it to perch at a given location, inaccessible with purely the thrust force of the platform. Our proposed approach is based on a hierarchical control framework, which allows different tasks to be executed according to their assigned priorities. An RL agent is then subsequently utilized to adjust the reference set-point of the lower-priority tasks to perform the swing-up maneuver, which is confined in the nullspace of the higher-priority tasks, such as maintaining a specific orientation and position of the end-effector. Our approach is validated using extensive numerical simulation studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13478 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework Das, Hemjyoti Vu, Minh Nhat Ott, Christian Robotics In this work, we present a novel approach to augment a model-based control method with a reinforcement learning (RL) agent and demonstrate a swing-up maneuver with a suspended aerial manipulation platform. These platforms are targeted towards a wide range of applications on construction sites involving cranes, with swing-up maneuvers allowing it to perch at a given location, inaccessible with purely the thrust force of the platform. Our proposed approach is based on a hierarchical control framework, which allows different tasks to be executed according to their assigned priorities. An RL agent is then subsequently utilized to adjust the reference set-point of the lower-priority tasks to perform the swing-up maneuver, which is confined in the nullspace of the higher-priority tasks, such as maintaining a specific orientation and position of the end-effector. Our approach is validated using extensive numerical simulation studies. |
| title | Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.13478 |