Research on the Unmanned Surface Vehicle Kinetics Model for Automatic Berthing

Zhang, Mengwei; He, Yuqing; Xiong, Junfeng

doi:10.1007/978-981-15-8155-7_305

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…According to the manuscript [32] by Hyo-Gon Kim, the obstacle avoidance of USVs can be divided into static obstacle avoidance and dynamic obstacle avoidance, among which dynamic obstacle avoidance includes three typical obstacle-avoidance scenarios, i.e., encountering, overtaking and crossing obstacle avoidance. Throughout these three scenarios, a line of sight (LOS) algorithm was used for USVs to avoid possible obstacle beyond the sea surface [33], and this algorithm was also selected to perform the obstacle-avoidance test.…”

Section: Virtual Testing Of Autonomous Navigation Performance Of Usvsmentioning

confidence: 99%

A Virtual System and Method for Autonomous Navigation Performance Testing of Unmanned Surface Vehicles

Xiao,

Zheng,

Tong

et al. 2023

JMSE

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An overall framework of the virtual testing system has been established based on the analysis of the virtual testing requirements for autonomous navigation performance of unmanned surface vehicles (USVs). This system consists of several modules, including the environment module, motion module, sensor module, and 3D visualization module. Firstly, within the robot operating system (ROS) environment, a three-dimensional navigation environment was generated by combining actual wave spectra with Gerstner waves. By designing a power plugin for USV navigation, the system was made to reflects the coupled motion model of USVs in wind, waves and currents, along with predictive results. Regarding the four typical sensor information on USVs, the actual sensors were virtualized, and a simulation approach for virtual sensor information is provided. The three-dimensional visualization of USV’s motion enables the intuitive display and analysis of the virtual testing process. Based on the prediction of coupled motion characteristics in wind, waves and currents, the interaction between USVs and the virtual testing system has been realized. A platform for virtual testing experiments to determine the autonomous navigation performance of USVs was established, and the effectiveness of the platform was verified in terms of perception and environmental interference. In virtual environmental interference validation, the average amplitude deviation of the heave motion of USVs under sea state 3 reaches 0.74 m, and the average amplitude deviation of the pitch motion reaches 0.25 rad, showing the gradually increasing disturbance of the sea state. Finally, virtual testing experiments were conducted on a specific USV to evaluate its autonomous navigation perception performance, trajectory tracking performance, and autonomous obstacle avoidance. The evaluation results indicate that the platform can achieve the functionality of virtual testing for the autonomous navigation performance of USVs from the perspective of cost function, taking the reaction distance, regression distance, and obstacle avoidance time into consideration. A representative example is that the cost function deviation rates of overtaking obstacle avoidance between static and dynamic seas reach 5.11%, 8.98% and 18.43%, respectively. The gradually increasing data shows that the virtual simulating method matches the drifting-off-course tendency of boats in rough seas. This includes acquiring perception information of navigation and simulating the motion and navigation processes for visualization. The platform provides new means for testing and evaluating the autonomous navigation performance of USVs.

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Section: Virtual Testing Of Autonomous Navigation Performance Of Usvsmentioning

confidence: 99%

A Virtual System and Method for Autonomous Navigation Performance Testing of Unmanned Surface Vehicles

Xiao,

Zheng,

Tong

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

Wu,

Xiao,

Cao

et al. 2024

J Intell Robot Syst

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This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.

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Path Planning Method of Unmanned Surface Vehicles Formation Based on Improved A* Algorithm

Sang

Xiao

Xiong

et al. 2023

JMSE

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Unmanned surface vehicle (USV) formation is a hot topic of current research. Path planning is the core technology for USV formation. This paper focuses on a USV formation path planning problem considering kinetic constraints. Firstly, an improved A* algorithm is proposed to solve the point-to-point path planning of a USV considering kinetic constraints. In this algorithm, the yaw constraint is introduced on top of the position constraint to extend the state space of the USV to three dimensions, and the convergence speed is accelerated by building a heuristic map. The dynamics model of the USV is used to generate the minimum trajectory elements to ensure that the path conforms to the kinetic constraints. Secondly, the mathematical model of USV formation based on the virtual structure method is established, and the path planning scheme of formation navigation and formation reconfiguration is given according to the improved A* algorithm. Finally, we carry out a USV model identification experiment for SL900 USV and simulation experiments based on the model. The experimental results show that the output path of the proposed method is smoother compared with the traditional method. This method can provide a globally safe path with kinetic constraints for USV formation navigation and formation reconstruction.

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Research on the Unmanned Surface Vehicle Kinetics Model for Automatic Berthing

Cited by 3 publications

References 11 publications

A Virtual System and Method for Autonomous Navigation Performance Testing of Unmanned Surface Vehicles

A Virtual System and Method for Autonomous Navigation Performance Testing of Unmanned Surface Vehicles

Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

Path Planning Method of Unmanned Surface Vehicles Formation Based on Improved A* Algorithm

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