A Path Planning Method for A-Uav Based on the CGRUA Model

Amphibious Unmanned Aerial Vehicle (A-UAV), equipped with multi-sensor systems, has significant value in digitization of water body by monitoring both aquatic and aerial domains. Compared to traditional methods, it offers improved multi-dimensional perception but faces challenges like complex operational paths and data fusion difficulties, which increase the risk of system failures. This study proposes a path planning model for A-UAV based on the CGRUA framework, combining drifting and flying paths to enhance endurance and measurement range. Using buoy data from the Global Drifter Program (GDP buoy ID 2101873), the accuracy of the hybrid CGRUA model predictions and the effectiveness of path planning was validated.

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Publication Details

Published
2025-06-30
DOI
https://doi.org/10.1109/icca65672.2025.11129840
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

A Path Planning Method for A-Uav Based on the CGRUA Model

Jun Qi, Xiangnan Qin, Jinjun Guo, Xing Xia
Robotic Path Planning Algorithms
article

A Path Planning Method for A-Uav Based on the CGRUA Model

Jun Qi, Xiangnan Qin, Jinjun Guo, Xing Xia
article en

Abstract

Amphibious Unmanned Aerial Vehicle (A-UAV), equipped with multi-sensor systems, has significant value in digitization of water body by monitoring both aquatic and aerial domains. Compared to traditional methods, it offers improved multi-dimensional perception but faces challenges like complex operational paths and data fusion difficulties, which increase the risk of system failures. This study proposes a path planning model for A-UAV based on the CGRUA framework, combining drifting and flying paths to enhance endurance and measurement range. Using buoy data from the Global Drifter Program (GDP buoy ID 2101873), the accuracy of the hybrid CGRUA model predictions and the effectiveness of path planning was validated.

Zhengzhou University (CN), Henan Province Water Conservancy Survey and Design Research (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 78%
Robotic Path Planning Algorithms
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