Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions
The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/6959 |
| _version_ | 1827677870191804416 |
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| author | Myron Papadimitrakis Marios Stogiannos Haralambos Sarimveis Alex Alexandridis |
| author_facet | Myron Papadimitrakis Marios Stogiannos Haralambos Sarimveis Alex Alexandridis |
| author_sort | Myron Papadimitrakis |
| collection | DOAJ |
| description | The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel. |
| first_indexed | 2024-03-10T05:52:46Z |
| format | Article |
| id | doaj.art-4646ade1a6ef43978960564136dc8ff2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:46Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4646ade1a6ef43978960564136dc8ff22023-11-22T21:34:29ZengMDPI AGSensors1424-82202021-10-012121695910.3390/s21216959Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory PredictionsMyron Papadimitrakis0Marios Stogiannos1Haralambos Sarimveis2Alex Alexandridis3Department of Electrical and Electronic Engineering, University of West Attica, 12241 Aigaleo, GreeceDepartment of Electrical and Electronic Engineering, University of West Attica, 12241 Aigaleo, GreeceSchool of Chemical Engineering, National Technical University of Athens, 15780 Zografou, GreeceDepartment of Electrical and Electronic Engineering, University of West Attica, 12241 Aigaleo, GreeceThe field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel.https://www.mdpi.com/1424-8220/21/21/6959autonomous vesselscollision avoidancemodel predictive controlradial basis function networkstrajectory optimization |
| spellingShingle | Myron Papadimitrakis Marios Stogiannos Haralambos Sarimveis Alex Alexandridis Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions Sensors autonomous vessels collision avoidance model predictive control radial basis function networks trajectory optimization |
| title | Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions |
| title_full | Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions |
| title_fullStr | Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions |
| title_full_unstemmed | Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions |
| title_short | Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions |
| title_sort | multi ship control and collision avoidance using mpc and rbf based trajectory predictions |
| topic | autonomous vessels collision avoidance model predictive control radial basis function networks trajectory optimization |
| url | https://www.mdpi.com/1424-8220/21/21/6959 |
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