A path planning algorithm for unmanned surface vessel with pose constraints in an unknown environment

Yu, Jiabin; Chen, Yang; Yang, Meng; Chen, Zhihao; Xu, Jiping; Lu, Yang; Zhao, Zhiyao

doi:10.1016/j.ijnaoe.2024.100602

International Journal of Naval Architecture and Ocean Engineeri

2024

DOI: 10.1016/j.ijnaoe.2024.100602

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A path planning algorithm for unmanned surface vessel with pose constraints in an unknown environment

Jiabin Yu,

Yang Chen,

Meng Yang

et al.

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2024

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Cited by 2 publications

References 28 publications

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A modified A* algorithm combining remote sensing technique to collect representative samples from unmanned surface vehicles

Wang,

Liu,

Wang

2024

Front. Neurorobot.

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Ensuring representativeness of collected samples is the most critical requirement of water sampling. Unmanned surface vehicles (USVs) have been widely adopted in water sampling, but current USV sampling path planning tend to overemphasize path optimization, neglecting the representative samples collection. This study proposed a modified A* algorithm that combined remote sensing technique while considering both path length and the representativeness of collected samples. Water quality parameters were initially retrieved using satellite remote sensing imagery and a deep belief network model, with the parameter value incorporated as coefficient Q in the heuristic function of A* algorithm. The adjustment coefficient k was then introduced into the coefficient Q to optimize the trade-off between sampling representativeness and path length. To evaluate the effectiveness of this algorithm, Chlorophyll-a concentration (Chl-a) was employed as the test parameter, with Chaohu Lake as the study area. Results showed that the algorithm was effective in collecting more representative samples in real-world conditions. As the coefficient k increased, the representativeness of collected samples enhanced, indicated by the Chl-a closely approximating the overall mean Chl-a and exhibiting a gradient distribution. This enhancement was also associated with increased path length. This study is significant in USV water sampling and water environment protection.

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A modified A* algorithm combining remote sensing technique to collect representative samples from unmanned surface vehicles

Wang,

Liu,

Wang

2024

Front. Neurorobot.

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Adaptive Sliding Mode Trajectory Tracking Control of Unmanned Surface Vessels Based on Time-Domain Wave Inversion

Mou,

Shen,

Zheng

2024

JMSE

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In this work, we develop a trajectory tracking control method for unmanned surface vessels (USVs) based on real-time compensation for actual wave disturbances. Firstly, wave information from the actual sea surface is extracted through stereoscopic visual observations, and data preprocessing is performed using a task-driven point cloud downsampling network. We reconstruct the phase-resolved wave field in real time. Subsequently, the wave disturbances are modeled mechanically, and real-time wave disturbances are used as feedforward inputs. Furthermore, an adaptive backstepping sliding mode control law based on command filters is designed to avoid differential explosion and mitigate sliding mode chattering. An adaptive law is also designed to estimate and compensate for other external disturbances and inversion error bounds that cannot be computed in real time. Finally, the feasibility of the proposed control strategy is validated through stability analysis and numerical simulation experiments.

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A path planning algorithm for unmanned surface vessel with pose constraints in an unknown environment

Cited by 2 publications

References 28 publications

A modified A* algorithm combining remote sensing technique to collect representative samples from unmanned surface vehicles

A modified A* algorithm combining remote sensing technique to collect representative samples from unmanned surface vehicles

Adaptive Sliding Mode Trajectory Tracking Control of Unmanned Surface Vessels Based on Time-Domain Wave Inversion

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