|Table of Contents|

Citation:
　Chuong Nguyen,Minh Tran,Trung-Tin Nguyen,et al.Simulation Framework for Addressing Challenges in Path Planning Evaluation for an Autonomous Surface Vehicle[J].Journal of Marine Science and Application,2025,(4):816-828.[doi:10.1007/s11804-025-00646-z]
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Simulation Framework for Addressing Challenges in Path Planning Evaluation for an Autonomous Surface Vehicle

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Title:
Simulation Framework for Addressing Challenges in Path Planning Evaluation for an Autonomous Surface Vehicle
Author(s):
Chuong Nguyen1;  Minh Tran1; 2;  Trung-Tin Nguyen3;  Nuwantha Fernando4;  Liuping Wang4;  Hung Nguyen2
Affilations:
Author(s):
Chuong Nguyen1;  Minh Tran1; 2;  Trung-Tin Nguyen3;  Nuwantha Fernando4;  Liuping Wang4;  Hung Nguyen2
1. School of Science, Engineering and Technology, RMIT University Vietnam, 700000, Vietnam;
2. Australian Maritime College, University of Tasmania 7250, Australia;
3. Faculty of Sustainable Design Engineering, University of Prince Edward Island, C1A 4P3, Canada;
4. School of Engineering, RMIT University 3001, Australia
Keywords:
Autonomous surface vehicle; Global path planner; Local path planner; Simulation; Robot operating system; Gazebo
分类号:
-
DOI:
10.1007/s11804-025-00646-z
Abstract:
An efficient algorithm for path planning is crucial for guiding autonomous surface vehicles (ASVs) through designated waypoints. However, current evaluations of ASV path planning mainly focus on comparing total path lengths, using temporal models to estimate travel time, idealized integration of global and local motion planners, and omission of external environmental disturbances. These rudimentary criteria cannot adequately capture real-world operations. To address these shortcomings, this study introduces a simulation framework for evaluating navigation modules designed for ASVs. The proposed framework is implemented on a prototype ASV using the Robot Operating System (ROS) and the Gazebo simulation platform. The implementation processes replicated satellite images with the extended Kalman filter technique to acquire localized location data. Cost minimization for global trajectories is achieved through the application of Dijkstra and A* algorithms, while local obstacle avoidance is managed by the dynamic window approach algorithm. The results demonstrate the distinctions and intricacies of the metrics provided by the proposed simulation framework compared with the rudimentary criteria commonly utilized in conventional path planning works.

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Memo

Memo:
Received date:2024-1-31;Accepted date:2024-8-29。<br>Foundation item:Supported by the funding from RMIT Internal Research Grant R1. The authors would like to thank Huy Luong, Trung Nguyen, Phong Nguyen, Hieu Ha, and Shehani Perera at RMIT University for their support in the project development.<br>Corresponding author:Chuong Nguyen,E-mail:chuong.nguyen4@rmit.edu.vn
Last Update: 2025-08-27