Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments
Docking of autonomous surface vehicles (ASVs) involves intricate maneuvering at low speeds under the influence of unknown environmental forces, and is often a challenging operation even for experienced helmsmen. In this paper, we propose an optimization-based trajectory planner for performing automa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9256302/ |
| _version_ | 1818734581511094272 |
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| author | Andreas B. Martinsen Glenn Bitar Anastasios M. Lekkas Sebastien Gros |
| author_facet | Andreas B. Martinsen Glenn Bitar Anastasios M. Lekkas Sebastien Gros |
| author_sort | Andreas B. Martinsen |
| collection | DOAJ |
| description | Docking of autonomous surface vehicles (ASVs) involves intricate maneuvering at low speeds under the influence of unknown environmental forces, and is often a challenging operation even for experienced helmsmen. In this paper, we propose an optimization-based trajectory planner for performing automatic docking of a small ASV. The approach formulates the docking objective as a nonlinear optimal control problem, which is used to plan collision-free trajectories. Compared to recent works, the main contributions are the inclusion of a map of the harbor and additional measurements from range sensors, such as LIDAR and ultrasonic distance sensors, to account for map inaccuracies as well as unmapped objects, such as moored vessels. To use the map and sensor data, a set generation method is developed, which in real-time computes a safe operating region, this is then used to ensure the planned trajectory is safe. To track the planned trajectory, a trajectory-tracking dynamic positioning controller is used. The performance of the method is tested experimentally on a small ASV in confined waters in Trondheim, Norway. The experiments demonstrate that the proposed method is able to perform collision-free docking maneuvers with respect to static obstacles, and achieves successful docking. |
| first_indexed | 2024-12-18T00:07:38Z |
| format | Article |
| id | doaj.art-8da429cf28f64333a89e077dad21c1e1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:07:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8da429cf28f64333a89e077dad21c1e12022-12-21T21:27:45ZengIEEEIEEE Access2169-35362020-01-01820497420498610.1109/ACCESS.2020.30371719256302Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and ExperimentsAndreas B. Martinsen0https://orcid.org/0000-0002-6047-1715Glenn Bitar1https://orcid.org/0000-0002-2989-8542Anastasios M. Lekkas2Sebastien Gros3https://orcid.org/0000-0001-6054-2133Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, NorwayDocking of autonomous surface vehicles (ASVs) involves intricate maneuvering at low speeds under the influence of unknown environmental forces, and is often a challenging operation even for experienced helmsmen. In this paper, we propose an optimization-based trajectory planner for performing automatic docking of a small ASV. The approach formulates the docking objective as a nonlinear optimal control problem, which is used to plan collision-free trajectories. Compared to recent works, the main contributions are the inclusion of a map of the harbor and additional measurements from range sensors, such as LIDAR and ultrasonic distance sensors, to account for map inaccuracies as well as unmapped objects, such as moored vessels. To use the map and sensor data, a set generation method is developed, which in real-time computes a safe operating region, this is then used to ensure the planned trajectory is safe. To track the planned trajectory, a trajectory-tracking dynamic positioning controller is used. The performance of the method is tested experimentally on a small ASV in confined waters in Trondheim, Norway. The experiments demonstrate that the proposed method is able to perform collision-free docking maneuvers with respect to static obstacles, and achieves successful docking.https://ieeexplore.ieee.org/document/9256302/Autonomous surface vehiclesberthingcollision avoidancedockingmarine vehiclesmotion planning |
| spellingShingle | Andreas B. Martinsen Glenn Bitar Anastasios M. Lekkas Sebastien Gros Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments IEEE Access Autonomous surface vehicles berthing collision avoidance docking marine vehicles motion planning |
| title | Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments |
| title_full | Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments |
| title_fullStr | Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments |
| title_full_unstemmed | Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments |
| title_short | Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments |
| title_sort | optimization based automatic docking and berthing of asvs using exteroceptive sensors theory and experiments |
| topic | Autonomous surface vehicles berthing collision avoidance docking marine vehicles motion planning |
| url | https://ieeexplore.ieee.org/document/9256302/ |
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