An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning

Liu, Chenguang; Mao, Qingzhou; Chu, Xiumin; Xie, Shuguo

doi:10.3390/app9061057

Cited by 113 publications

(65 citation statements)

References 16 publications

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“…A-Star algorithm is a heuristic algorithm, as shown in Figure 12, which can determine the search scope based on the estimated value of the current point-to-end road resistance, making the search point closer and closer to the optimal point. A-Star algorithm is widely used in the scenarios of autonomous driving path planning and vehicle navigation [31]. Here we select the A-Star algorithm as the main solver, the road resistance and the traffic network obtained by the above equations are taken as inputs like matrix (21) and matrix (24), and the A-Star algorithm can output the best driving path.…”

Section: The Planning Strategy Of the Optimal Coupling Path Between Ementioning

confidence: 99%

Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information

et al. 2019

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Battery-powered electric vehicles (EVs) have a limited on-board energy storage and present the problem of driving mileage anxiety. Moreover, battery energy storage density cannot be effectively improved in a short time, which is a technical bottleneck of EVs. By considering the impact of traffic information on energy consumption forecasting, an energy-saving path planning method for EVs that takes traffic information into account is proposed. The modeling process of the EV model and the construction process of the traffic simulation model are expounded. In addition, the long-term, short-term memory neural network (LSTM) model is selected to predict the energy consumption of EVs, and the sequence to sequence technology is used in the model to integrate the driving condition data of EVs with traffic information. In order to apply the predicted energy consumption to travel guidance, a road planning method with the optimal coupling of energy consumption and distance is proposed. The experimental results show that the energy-based economic path uses 9.9% lower energy consumption and 40.2% shorter travel time than the distance-based path, and a 1.5% lower energy consumption and 18.6% longer travel time than the time-based path.

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Section: The Planning Strategy Of the Optimal Coupling Path Between Ementioning

confidence: 99%

Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information

et al. 2019

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“…The route planning algorithm is one of the aspects of obstacle avoidance system for unmanned surface vehicle. Liu, et al proposed an improved A-Star algorithm for planning path for vessels, which integrated takes of route length, obstacle dynamics, navigation rules, and maneuverability restriction [27]. Particularly, the current of the sea is considered in the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Ship Route Planning Based on Double-Cycling Genetic Algorithm Considering Ship Maneuverability Constraint

et al. 2020

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In order to complete automatic route planning in the complex navigation environment, this paper proposes a quadratic optimization genetic algorithm combining the motion characteristics of the ships. Furthermore, the constraints of the maneuvering characteristics of ship are considered to calculate the accurate planning routes fast. First of all, the turning and speed reduction model of ships are established, which is the foundation for the accurate calculation of approach states between the own ship and target ships. Second, in order to check the position relationship between the planned route and the land objects (such as the islands and rocks) on the chart, a detection algorithm between lines and irregular polygon boundary based on quadtree method is acquired. Third, a new genetic algorithm based on double-cycling optimization is proposed by this paper. Aiming at the complex problem of comprehensive collision avoidance planning under the conditions of static and dynamic obstacles, this paper realizes an efficient and feasible solution of dynamic programming for ship route. Finally, the algorithm is verified on a test fireboat, and the experiment data prove the feasibility and effectiveness of the proposed algorithm by this paper.

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“…Based on the latest report of the International Maritime Organization (IMO) [1], 90% of goods are currently transported by shipping. Maritime vessel traffic differs from ground-based traffic because of the complexity of rapidly changing the direction of the ships on the water [2]. In order to ensure that the ship passes safely through the waters, the water depth should be greater than the sum of the draft of the ship and surplus under keel clearance (UKC) [3].…”

Section: Introductionmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning

Cited by 113 publications

References 16 publications

Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information

Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information

Ship Route Planning Based on Double-Cycling Genetic Algorithm Considering Ship Maneuverability Constraint

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

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