Random Patrol Path Planning for Unmanned Surface Vehicles in Shallow Waters

Xie, Xiyang; Wang, Yunliang; Wu, Yanjuan; You, Mankun; Zhang, Shuangyang

doi:10.1109/icma54519.2022.9856340

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Also, the researchers of [63] utilize the RRT* and dynamic window approach (DWA) algorithms for path planning. The proposed method efficiently combines global and local planning to explore the water surface while ensuring USV safety.…”

Section: Rrt and Rrt* Algorithmsmentioning

confidence: 99%

A Review of Path Planning Methods for Marine Autonomous Surface Vehicles

Wu,

Wang,

Liu

2024

JMSE

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A marine autonomous surface vehicle (ASV) is a kind of autonomous marine robot with intelligent and flexible use advantages. They are mainly divided into two categories: unmanned vessels and unmanned sailboats. Marine ASVs are essential in marine science, industry, environmental protection, and national defense. One of the primary challenges faced by marine ASVs is autonomously planning paths in an intricate marine environment. Numerous research findings have surfaced in recent years, including the combination with popular machine learning. However, a systematic literature review is still lacking, primarily a comprehensive comparison of two types of ASV path planning methods. This review first introduces the problem and evaluation indicators of path planning for ASVs. Then, aiming at unmanned vessels and sailboats, respectively, it sorts out various path planning algorithms proposed in the existing literature, including the advantages and limitations of both kinds of ASVs, and discusses them in combination with evaluation indicators. Also, this paper explores how marine environmental factors affect path planning and its corresponding treatment methods. Finally, this review summarizes the challenges of unmanned ship path planning, proposes potential technical solutions and future development directions, and aims to provide references for further development in this field.

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Section: Rrt and Rrt* Algorithmsmentioning

confidence: 99%

A Review of Path Planning Methods for Marine Autonomous Surface Vehicles

Wu,

Wang,

Liu

2024

JMSE

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“…As robotic technology advances, a mobile robot faces multi-target path planning problems in numerous circumstances [ 6 , 7 , 8 , 9 ]. The usual optimization criteria of the planning algorithm are the path length or the time cost [ 10 , 11 , 12 ]. There are other kinds of criteria for the path.…”

Section: Introductionmentioning

confidence: 99%

Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

Zhao

Misyurin

et al. 2023

Sensors

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The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path’s odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot’s pose and position data.

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