Adaptive High-Speed Echo Data Acquisition Method for Bathymetric LiDAR

Zhou, Guoqing; Jia, Guoshuai; Zhou, Xiang; Song, Naihui; Wu, Jinhuang; Gao, Ke; Huang, Jing; Xu, Jiasheng; Zhu, Qiang

doi:10.1109/tgrs.2024.3386687

Cited by 1 publication

(1 citation statement)

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“…The unmanned surface vessels (USVs) have a multitude of applications in bathy-metric mapping [1][2][3]. A bathymetric light detection and ranging (LiDAR), named "GQ-Cormorant 19," developed by our research team has been assembled on an un-manned surface vessel (USV), named "GQ-S20," for near shore three-dimensional (3-D) bathymetric mapping [4][5][6][7][8][9]. To ensure more than 30% overlap of the neighboring cross-strips and satisfy the accuracy requirements of bathymetric mapping of the point cloud data, a path-tracking functionality (system) must be developed for autonomous USV operation under different water environments, such as lakes, reservoirs, and seas [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development of an Adaptive Fuzzy Integral-Derivative Line-of-Sight Guidance Law for Bathymetric LiDAR Onboard USV

Zhou,

Wu,

Gao

et al. 2024

Preprint

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Previous control methods developed by our research team cannot satisfy the high-accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) guidance law for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle, with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain a high-accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS guidance law can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, and reduce the average cross-track error by 10.91%, and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional method.

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