An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules

Rong, Zhen; Gu, Qiyong; Shi, Ziqiang; Suo, Yongfeng

doi:10.3390/jmse11071439

Cited by 19 publications

(4 citation statements)

References 42 publications

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“…More recently, a ship path planning approach that considers multiple safety factors has been developed. This approach enhances ship navigation safety by taking into account environmental impacts, traffic regulations, and ship maneuvering constraints [35].…”

Section: Related Workmentioning

confidence: 99%

Global Path Planning for Autonomous Ship Navigation Considering the Practical Characteristics of the Port of Ulsan

Yun,

Kim,

Kim

et al. 2024

JMSE

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This study introduces global path planning for autonomous ships in port environments, with a focus on the Port of Ulsan, where various environmental factors are modeled for analysis. Global path planning is considered to take place from departure to berth, specifically accounting for scenarios involving a need to navigate via anchorage areas as waypoints due to unexpected increases in port traffic or when direct access to the berth is obstructed. In this study, a navigable grid for autonomous ships was constructed using land, breakwater, and water depth data. The modeling of the Port of Ulsan’s traffic lanes and anchorage areas reflects the port’s essential maritime characteristics for global path planning. In this study, an improved A* algorithm, along with grid-based path planning, was utilized to determine a global path plan. We used smoothing algorithms to refine the global paths for practical navigation, and the validation of these paths was achieved through conducting ship maneuvering simulations from model tests, which approximate real-world navigation in navigational simulation. This approach lays the groundwork for enhanced route generation studies in complex port environments.

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Section: Related Workmentioning

confidence: 99%

Global Path Planning for Autonomous Ship Navigation Considering the Practical Characteristics of the Port of Ulsan

Yun,

Kim,

Kim

et al. 2024

JMSE

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“…Therefore, He et al [7] improved the A-star algorithm by considering the dynamic search mechanism of the time factor so that the ship can generate a more reasonable dynamic obstacle avoidance path. Zhen et al [8] analyzed the factors that affect ship navigation safety and designed the turning model and smoothing method to improve the A-star algorithm so that the path could effectively avoid the shallow water area. Liang et al [9] improved the A-star algorithm by setting a safe distance from obstacles and removing unnecessary waypoints.…”

Section: Related Workmentioning

confidence: 99%

Soft Actor-Critic and Risk Assessment-Based Reinforcement Learning Method for Ship Path Planning

Wang,

Ji,

2024

Sustainability

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Ship path planning is one of the most important themes in waterway transportation, which is deemed as the cleanest mode of transportation due to its environmentally friendly and energy-efficient nature. A path-planning method that combines the soft actor-critic (SAC) and navigation risk assessment is proposed to address ship path planning in complex water environments. Specifically, a continuous environment model is established based on the Markov decision process (MDP), which considers the characteristics of the ship path-planning problem. To enhance the algorithm’s performance, an information detection strategy for restricted navigation areas is employed to improve state space, converting absolute bearing into relative bearing. Additionally, a risk penalty based on the navigation risk assessment model is introduced to ensure path safety while imposing potential energy rewards regarding navigation distance and turning angle. Finally, experimental results obtained from a navigation simulation environment verify the robustness of the proposed method. The results also demonstrate that the proposed algorithm achieves a smaller path length and sum of turning angles with safety and fuel economy improvement compared with traditional methods such as RRT (rapidly exploring random tree) and DQN (deep Q-network).

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“…Among the algorithms with potential applications in planning the route of maritime autonomous vehicles, various derivatives of the classic A* pathfinding algorithm for both seagoing vessels [19,20] and inland vessels [21,22] are very popular, while the remaining algorithms are usually based on evolutionary methods [23,24], potential fields methods [25], artificial neural networks [26], anticollision neural networks [27], methods using Linear Matrix Inequalities (LMIs) [28], or Rapid Random Tree (RRT) methods [29].…”

Section: Autonomous Ship Navigationmentioning

confidence: 99%

Modeling of an Autonomous Electric Propulsion Barge for Future Inland Waterway Transport

Łebkowski,

Koznowski

2023

Energies

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International trade is continuously rising, leading to an increase in the flow of goods passing through transportation hubs, including air and sea. In addition, the aging fleet of inland vessels necessitates renewal through the construction of new vessels, presenting opportunities for the adoption of modern transport technologies. Autonomous barges can transport bulk and containerized cargo between the central port of a specific region and smaller satellite ports, enabling the dispersal of goods over a wider area. Equipping autonomous barges with advanced sensors, such as LIDAR, computer vision systems that operate in visible light and thermal infrared, and incorporating advanced path finding and cooperation algorithms may enable them to operate autonomously, subject only to remote supervision. The purpose of this study is to explore the potential of autonomous electric propulsion barges in inland waterway transport. Given the increasing demand for efficient and sustainable transport solutions as a result of various new policies, which have set new ambitious goals in clean transportation, this study aims to develop a proposition of an electric propulsion hybrid drive inland waterway barge, and compare it to a conventional diesel-powered barge. The methodology involves the creation of a simulation model of an inland waterway class IV electric barge, equipped with advanced sensors and autonomous control systems. The barge’s navigation is managed through a multi-agent system, with evolutionary algorithms determining a safe passage route. This research also utilizes a proprietary networked ship traffic simulator, based on real inland vessel recorded routes, to conduct the autonomous navigation study. The energy consumption of the barge on a route resulting from the ship traffic simulation is then examined using the mathematical model using the OpenModelica package. As a result of the study, the proposed hybrid propulsion system achieved a 16% reduction in fuel consumption and CO2 emissions, while cutting engine operation time by more than 71%. The findings could provide valuable insights into the feasibility and efficiency of autonomous electric propulsion barges, potentially helping future developments in inland waterway transport.

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An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules

Cited by 19 publications

References 42 publications

Global Path Planning for Autonomous Ship Navigation Considering the Practical Characteristics of the Port of Ulsan

Global Path Planning for Autonomous Ship Navigation Considering the Practical Characteristics of the Port of Ulsan

Soft Actor-Critic and Risk Assessment-Based Reinforcement Learning Method for Ship Path Planning

Modeling of an Autonomous Electric Propulsion Barge for Future Inland Waterway Transport

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