Jiyu Yao’s research while affiliated with Dalian Maritime University and other places

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Publications (2)


Figure 1. Algorithmic flowchart of multi-ship collaborative collision avoidance path.
Figure 3. Ship Dubins curve model.
Figure 4. Obstacle expansion map.
Figure 5. Ship conflict node search graph.
Figure 6. Space-time dynamic obstacles.

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Ship Collaborative Path Planning Method Based on CS-STHA
  • Article
  • Full-text available

October 2022

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63 Reads

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6 Citations

Jiyu Yao

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Longhui Gang

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships.

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Citations (2)


... The complex and varied task environment increases the need for maritime resupply when ships conduct missions such as long-distance transportation, patrolling, and humanitarian aid, among others. Ship replenishment path planning [1][2][3] is a crucial task for ensuring the support of maritime operations. Its core objective is to reasonably plan the routes between ships and replenishment points, ensuring the safety of replenishment while completing the replenishment tasks in the shortest time possible. ...

Reference:

Research on Ship Replenishment Path Planning Based on the Modified Whale Optimization Algorithm
Ship Collaborative Path Planning Method Based on CS-STHA

... Among the frequently occurred maritime accidents such as fire/explosion, foundering/sinking, and stranding/ grounding, collisions receive the widest attention from both the shipping industry and the academic scholars due to their high frequency and the severe consequence they cause . Among studies on ship collision analysis and prediction based on ML models, SVMs are adopted to predict ship future navigation status regarding course and speed (Gang et al., 2021) and to divide the ship encounter azimuth map so as to avoid collisions (Gao et al., 2020). Multilayer perceptron (MLP) as a type of neural network, together with the fuzzy logic, is combined with classic expert system for collision avoidance (Ahn et al., 2012). ...

Decision-making of Vessel Collision Avoidance Based on Support Vector Regression
  • Citing Conference Paper
  • May 2021