Lunping Zhang
Research Subjects
Research Interests
Lunping Zhang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Guifu Tan
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Hongguang Lyu
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Jian Song
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Xiaofeng Sun
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Xiaoru Ma
Navigation College, Dalian Maritime University, Dalian, Liaoning, China
1 shared publication - Xikai Kang
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication
Latest Publications
- Open Access
- 31 July 2024
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 12 (8) , 1289
Identification of Complex Multi-Vessel Encounter Scenarios and Collision Avoidance Decision Modeling for MASSs
Complex multi-vessel encounter situations are a challenging problem for ships to avoid collisions, and the International Regulations for Preventing Collision at Sea, 1972 (COLREGs) do not provide a clear delineation of multi-vessel encounter situations and the responsibility of collision avoidance (CA). Furthermore, Marine Autonomous Surface Ships (MASS), which realize autonomous navigation functions, face the problem of recognizing complex multi-ship encounter situations and the corresponding CA decisions. In this study, we adopt the velocity obstacle (VO) algorithm to visualize and identify the danger of multi-ship encounters with the own ship (OS) as the first viewpoint. Additionally, we consider the motion changes in target ships (TSs) and their possible CA behaviors as the basis of the ship’s CA decision-making. According to COLREGs, a simplified method for classifying the encounter situations of multiple clustered ships is proposed, considering the coupling of collision hazards and CA responsibilities between related TSs. On this basis, the corresponding CA decisions for each classified situation are proposed, and a large number of simulation experiments are conducted based on the proposed method by considering the three-ship and four-ship encounter model in the Imazu problem as an example. The experimental results indicate that the proposed method can effectively recognize the complex multi-ship encounter situation in the Imazu problem, and it can adjust the CA measures of the OS in time according to the COLREGs and the behavior of TSs. This provides the basis and reference for MASS when facing complex multi-ship encounter situations.
- 31 July 2024
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 12 (8) , 1289
Open AccessIdentification of Complex Multi-Vessel Encounter Scenarios and Collision Avoidance Decision Modeling for MASSs
Complex multi-vessel encounter situations are a challenging problem for ships to avoid collisions, and the International Regulations for Preventing Collision at Sea, 1972 (COLREGs) do not provide a clear delineation of multi-vessel encounter situations and the responsibility of collision avoidance (CA). Furthermore, Marine Autonomous Surface Ships (MASS), which realize autonomous navigation functions, face the problem of recognizing complex multi-ship encounter situations and the corresponding CA decisions. In this study, we adopt the velocity obstacle (VO) algorithm to visualize and identify the danger of multi-ship encounters with the own ship (OS) as the first viewpoint. Additionally, we consider the motion changes in target ships (TSs) and their possible CA behaviors as the basis of the ship’s CA decision-making. According to COLREGs, a simplified method for classifying the encounter situations of multiple clustered ships is proposed, considering the coupling of collision hazards and CA responsibilities between related TSs. On this basis, the corresponding CA decisions for each classified situation are proposed, and a large number of simulation experiments are conducted based on the proposed method by considering the three-ship and four-ship encounter model in the Imazu problem as an example. The experimental results indicate that the proposed method can effectively recognize the complex multi-ship encounter situation in the Imazu problem, and it can adjust the CA measures of the OS in time according to the COLREGs and the behavior of TSs. This provides the basis and reference for MASS when facing complex multi-ship encounter situations.