A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

Melo, Juliana; Matos, Aníbal

doi:10.1002/asjc.2107

Cited by 10 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cohen et al [74] proposed an end-to-end deep learning approach to estimate the velocity of an AUV instead of a traditional filter, using only INS and DVL, which improved the effectiveness by 66-87% over the traditional model-based approach. Melo et al [75] proposed a data-driven particle filter, where sonar sensors are limited and cannot be modeled. The method learns from previous data to obtain an approximate estimated distribution, and it has relatively high accuracy and efficiency with a 40% lower data resampling frequency and 17% less running time than the traditional method.…”

Section: ) Feature Extractionmentioning

confidence: 99%

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

et al. 2023

View full text Add to dashboard Cite

The prerequisite for underwater vehicles to accomplish various underwater tasks is to obtain real-time position information about themselves in the underwater environment using internal or external sensors, which is known as underwater positioning and navigation technology. This paper presents a general review of underwater vehicle positioning and navigation technology and illustrates the importance of underwater terrain-aided navigation technology. It also summarizes its general structure and describes the principles and functions of each component. The paper mainly analyzes various mainstream methods applied to underwater terrain-aided navigation, focuses on terrain suitability analysis and terrain matching algorithms, which play a central role in the analysis. Additionally, the research progress of terrain-aided navigation technology is described, and the research methods of terrain suitability analysis, terrain matching navigation, and the latest technology applications are compared. Some shortcomings of the current underwater terrain-aided navigation technology are presented, and future research directions are foreseen to provide a basis for subsequent research.

show abstract

Section: ) Feature Extractionmentioning

confidence: 99%

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Several approaches have been proposed using neural networks [35][36][37][38], the support vector machine [39][40][41], hidden Markov model [42][43][44], fuzzy inference system [45,46], relevance vector machine [47,48], and long short-term memory networks [49][50][51]. Most of them are based on data-driven approaches [52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Robust tool wear monitoring system development by sensors and feature fusion

Lin

Lee

2022

Asian Journal of Control

View full text Add to dashboard Cite

This study introduces a tool wear monitoring system that uses multiple sensors and a feature fusion technique. To improve the robustness of the system, different tightening torque and spindle speed conditions were considered in the experimental design stage. Vibration signals in three coordinates and sound signals were collected and transformed by fast Fourier transform for feature extraction. Two types of features in the frequency domain were used to establish the proposed system, including the mean value features selected by the class mean scatter feature selection criterion and statistical features by Gradient Class Activation Mapping++ (Grad-CAM++). The proposed monitoring system was established using a hierarchical neural network structure and feature fusion of the sensors. Cross-validation was introduced to demonstrate the performance and effectiveness of our approach under various tightening torque values and spindle speeds.

show abstract

“…By doing so, they need to explicitly model all the disturbances that might affect the system. Such characteristics are particularly suited for terrain‐based navigation for sensor‐limited AUVs, where typical scenarios often include non‐negligible sources of noise affecting the system, which are unknown and hard to model [4]. Louise proposed a sliding mode control (SMC) law, specifically the super‐twisting algorithm with adaptive gains, for the trajectory tracking of the underwater swimming manipulator centre of mass.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system

Hou

Wang

et al. 2022

IET Control Theory & Appl

View full text Add to dashboard Cite

To deal with thrusters' faults of autonomous underwater vehicle (AUV), an iterative learning algorithm fault-tolerant control (FTC) based on the linear extended states observer (LESO) is proposed. In this control scheme, the non-linear feedback mechanism of the LESO is transplanted into iterative learning processes to estimate fault. Compared to our previous work, LESO is used to substitute classic non-linear extended state observer to make the establishment of the whole system more structured; moreover, the number of parameters need to be tuned can be reduced by the conception of observer bandwidth of LESO. To enhance the controllability and robustness of whole scheme, a new saturated sliding mode controller is proposed based on the Lyapunov theory. Then to achieve online parameter self-tuning for the control system, fuzzy logic controllers are introduced to find optimal relationship between LESO's parameter and tracking errors. The performance of the proposed controller is tested by some comparison experiments on Zhuhai A18D AUV; the results show that the proposed control scheme can ensure better stability than classical control and our previous control scheme when AUV suffers faults.

show abstract

A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

Cited by 10 publications

References 19 publications

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

Robust tool wear monitoring system development by sensors and feature fusion

Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system

Contact Info

Product

Resources

About