Terrain-assisted navigation of long-range AUV based on intelligent particle filter

Chai, Xiujun; Li, Yuanlong; Qiao, Lei

doi:10.23919/ascc56756.2022.9828323

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Consequently, our further research are needed. Here, to address issues mentioned above, we derive inspiration from IPF [8,9], an attempt was made to achieve better localization accuracy by integrating, i.e. AIPF.…”

Section: Introductionmentioning

confidence: 99%

Adaptive intelligent particle filter for visual localization of mobile robot

Li,

Wang,

Chen

et al. 2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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In the field of unmanned ships, which has attracted widly attention in recent years, the small terminals distributed on the huge ship need to independently judge the collision risks. However, the performance of terminal devices is significantly limited to ambient noise which affect the docking guidance. In this paper, Adaptive Intelligent Particle Filter (AIPF), an assisted tracking method based on Intelligent Particle Filter (IPF) is proposed to smooth and continuous state observation, thus effectively eliminating abnormal and leaping data. Specifically, this method regards the coordinates of the target in a period of time as a process with stationary independent increment, and the possible motion model is explored from an engineering perspective, so as to optimize low weight particles and adjust the number of particles dynamically. The experimental results on the ARMv7 platform demonstrate that even under the harsh conditions of computational power, the suggested method can still reduce particle degeneracy, improve the performance of particle swarm to better achieve auxiliary tracking.

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Section: Introductionmentioning

confidence: 99%

Adaptive intelligent particle filter for visual localization of mobile robot

Li,

Wang,

Chen

et al. 2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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Underwater multizonotope terrain‐aided navigation method with coarse map based on set‐membership filter

Ma,

et al. 2024

Journal of Field Robotics

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Terrain‐aided navigation (TAN) is a viable method to achieve long‐term underwater navigation for long‐range autonomous underwater vehicles (AUVs). However, the high‐accuracy positioning results of most TAN systems rely on precise a priori seabed terrain maps, which restricts their applicability to a few areas with accurate bathymetric measurements of the seabed terrain. This article introduces a TAN system based on the General Bathymetric Chart of the Oceans (GEBCO) data set for global marine applications. Specifically, to address the low accuracy and poor robustness of the TAN system with imprecise bathymetric measurement and low‐resolution data from the GEBCO data set, this article proposes a multizonotope TAN method based on set‐membership filter (SMF) theory. The SMF theory is employed to handle the unknown distribution of the measurement noise from the GEBCO data set, introducing a multizonotope measurement update model to achieve more precise navigational results while addressing the perceptual ambiguity caused by self‐similar terrain. The smoothness of the terrain is incorporated as a parameter in the generation ranges of multizonotope, enabling adaptive adjustment based on terrain smoothness to reduce costs and enhance navigational performance. The accuracy and robustness of the proposed method are verified through all shipboard experiments, publicly available data sets, and AUV experiments. Compared with state‐of‐the‐art TAN methods, the average and maximum positioning errors have decreased by 64.83% and 48.84%, respectively. Finally, based on the experimental results, a preliminary distribution of suitable areas in the oceans is provided.

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