Inland Waterway Ship Path Planning Based on Improved RRT Algorithm

Cao, Shengshi; Ping-yi, Fan; Yan, Tao; Xie, Cheng; Deng, Jian; Xu, Feng; Shu, Yu

doi:10.3390/jmse10101460

Cited by 25 publications

(8 citation statements)

References 39 publications

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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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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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“…Firstly, considering that genetic algorithm (GA) parameter settings are relatively few and easy to adjust, this study adopts a genetic algorithm to solve the problem of path planning for unmanned ships. In [18], improvements are made to the A* algorithm and the D* algorithm by incorporating turning angle information, introducing corresponding collision avoidance functions, and other methods to reduce the issues of excessive search nodes and waypoints in path planning. These algorithmic enhancements aim to achieve the shortest path length or reduce planning time.…”

Section: Related Workmentioning

confidence: 99%

“…To validate the effectiveness of the proposed multi-objective optimization method based on the genetic algorithm, the initial population size is set as M=50 and the algebra of terminating evolution G=50 based on the same grid map, and the path search effect of this paper's algorithm is compared with the traditional particle swarm algorithm [35]that solely considers path length and traditional A* algorithm [36], and the results are shown in Fig. 11.…”

Section: B the Algorithm Of This Paper In Comparison With Other Tradi...mentioning

confidence: 99%

Research on Automatic Trajectory Planning Method of Unmanned Ships Based on Multi-Objective Optimization

Fu,

Qian

2023

IEEE Access

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For unmanned ships' autonomous navigation and water traffic systems, trajectory planning is a critical problem. Because conventional optimization techniques only consider a single target, such as the shortest trajectory distance or lowest time, multi-objective optimization can generate trajectory solutions that are optimized in terms of both distance and smoothness, thereby better serving the control of unmanned vessels in real-world applications. To achieve this, we propose a multi-objective non-linear trajectory planning method based on an improved genetic algorithm. This method simultaneously considers both the trajectory distance and smoothness, while incorporating constraints related to the safety characteristics and inherent properties of the unmanned ship, including speed and steering angles. According to simulation data, the proposed multi-objective improved genetic algorithm in this paper is compared to the single-objective genetic algorithm that only considers path length in various environments, resulting in an average reduction of 18.4% in the number of turning points and 26.38% in average turning angles in the planned paths. Compared to the traditional A* algorithm and PSO algorithm, it achieved an average reduction of 49.9% in the number of turning points and 78.48% in average turning angles. In practical cases, it demonstrated an average reduction of 30.47% in the number of turning points and 35.49% in average turning angles compared to other algorithms.

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“…The obstacles in the navigation environment are classified with different potential field functions and the optimal path is obtained by the minimum navigation risk points. Literature [2] proposed an improved rapidly random tree algorithm for ship path planning in complex navigation environments of inland waterways. Literature [3] proposed an improved ant colony optimization algorithm with fuzzy logic to solve the local path planning problem of dynamic obstacle avoidance in the complex environment.…”

Section: Introductionmentioning

confidence: 99%

Research on Global Ship Path Planning Method Based on Improved Ant Colony Algorithm

Zhang

Ren

Zhou

2023

IEEE Open J. Intell. Transp. Syst.

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To solve the global path planning problem of the ship in the static and dynamic environment, we propose an improved ant colony algorithm to plan the ship's navigation path. We use the artificial potential field method to compute the force direction of the ship at the initial iteration stage. The attraction potential field function is modified to improve the iteration efficiency of the hybrid ant colony algorithm. We design the pseudo-random state transition rule and improve the convergence of the hybrid algorithm by strengthening the selection of good paths. When updating the pheromone, we consider the path's length, safety, and smoothness to plan a safer navigation path. The simulation results show that the improved ant colony algorithm has a faster convergence speed than the original ant colony algorithm. The optimal solution quality is higher, which can realize global ship path planning in static and dynamic environments.

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Inland Waterway Ship Path Planning Based on Improved RRT Algorithm

Cited by 25 publications

References 39 publications

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

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

Research on Automatic Trajectory Planning Method of Unmanned Ships Based on Multi-Objective Optimization

Research on Global Ship Path Planning Method Based on Improved Ant Colony Algorithm

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