scholarly journals Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 370 ◽  
Author(s):  
Mingwei Sheng ◽  
Songqi Tang ◽  
Hongde Qin ◽  
Lei Wan
Keyword(s):  
Nearest Neighbor ◽  
Autonomous Underwater Vehicles ◽  
Data Association ◽  
Underwater Vehicles ◽  
Near Neighbor ◽  
Cluster Center ◽  
Model Data ◽  
Sea Trial ◽  
Underwater Target ◽  
Imaging Sonar

Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

2020 ◽  
Vol 42 (11) ◽  
pp. 1946-1959
Author(s):  
Jiayu He ◽  
Ye Li ◽  
Jian Cao ◽  
Yueming Li ◽  
Yanqing Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Gaussian Kernel ◽  
Filter Method ◽  
Grey Prediction ◽  
Sea Trial ◽  
False Diagnosis

The overall architectural complexity of autonomous underwater vehicles continuous to increase, enlarging the probability of fault occurrence in subsystems. Estimating the thrust loss by particle filter provided a useful method to detect the fault in propeller subsystem. In order to detect the fault in propellers as early as possible, the particle filter direct prediction method could amplify the fault trend and detect the fault earlier, but at the same time increase the possibility of false diagnosis. Therefore, a more accurate fault diagnosis method was required to discover the fault early and decrease the occurrence of false diagnosis. In this paper, an improved particle filter prediction method was proposed, combining the advantage of grey prediction to forecast the motion state, reducing the uncertainty in particle filter direct prediction process. Besides, the Gaussian kernel function was applied to judge the credibility of the prediction result, decreasing the possibility of the false diagnosis. In the experiments with simulated working conditions data and a section of actual sea trial data with propeller fault, the proposed method detected the fault earlier compared with the original particle filter method, and reduced the false diagnosis rate compared with the particle filter direct prediction method. The results show that the proposed method is effective in detecting the fault early with low false diagnosis.

scholarly journals Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 102 ◽  
Author(s):  
Tiedong Zhang ◽  
Shuwei Liu ◽  
Xiao He ◽  
Hai Huang ◽  
Kangda Hao
Keyword(s):  
Target Tracking ◽  
Autonomous Underwater Vehicles ◽  
Region Growing ◽  
Underwater Vehicles ◽  
Generalized Regression Neural Network ◽  
Cluttered Environments ◽  
Online Processing ◽  
Underwater Target ◽  
Region Growing Segmentation ◽  
Forward Looking

In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

Development of an ultra short baseline–aided buoy for underwater targets localization

2019 ◽  
Vol 233 (4) ◽  
pp. 1212-1225 ◽  
Author(s):  
Francesco Fanelli ◽  
Niccolò Monni ◽  
Nicola Palma ◽  
Alessandro Ridolfi
Keyword(s):  
Measurement Errors ◽  
Autonomous Underwater Vehicles ◽  
Dynamic Modelling ◽  
Experimental Tests ◽  
Underwater Vehicles ◽  
Short Baseline ◽  
Vehicle Localization ◽  
Positioning Systems ◽  
Fusion Methods ◽  
Underwater Target

Autonomous underwater vehicles localization and navigation are challenging due to the lack of Global Positioning System underwater: alternative techniques have then to be used in order to measure the position of the vehicle. To this aim, sensor fusion methods based on acoustic positioning systems are often exploited. This article faces the study and the improvement of the localization of an underwater target through an ultra short baseline–aided buoy built by the Mechatronics and Dynamic Modelling Laboratory of the University of Florence. Such a buoy relies on an ultra short baseline device for the localization and is aided by a proper sensor set in order to compensate variations in its pose. First, a study of the underwater localization based on the ultra short baseline technique is provided. The measurement errors entailed by the buoy motion are then analyzed and preliminarily compensated, exploiting linear least squares methods. Subsequently, filtering techniques are considered with the aim to further increase the accuracy of the ultra short baseline measurements. Due to the nonlinearities of the sensors characteristics, extended Kalman filter has been used, with different models for stationary and moving targets. The solutions proposed have been validated through experimental tests conducted with MArine Robotic Tool for Archaeology autonomous underwater vehicles built by the Mechatronics and Dynamic Modelling Laboratory. The results evidence an improved vehicle localization, suggesting interesting future developments concerning both mechanical and computational solutions.

AUV Bathymetric Simultaneous Localisation and Mapping Using Graph Method

2019 ◽  
Vol 72 (06) ◽  
pp. 1602-1622
Author(s):  
Teng Ma ◽  
Ye Li ◽  
Yusen Gong ◽  
Rupeng Wang ◽  
Mingwei Sheng ◽  
...  
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Data Association ◽  
Underwater Vehicles ◽  
Geological Surveys ◽  
Underwater Navigation ◽  
Graph Slam ◽  
Mapping Techniques ◽  
Terrain Matching ◽  
Global And Local

Although topographic mapping missions and geological surveys carried out by Autonomous Underwater Vehicles (AUVs) are becoming increasingly prevalent, the lack of precise navigation in these scenarios still limits their application. This paper deals with the problems of long-term underwater navigation for AUVs and provides new mapping techniques by developing a Bathymetric Simultaneous Localisation And Mapping (BSLAM) method based on graph SLAM technology. To considerably reduce the calculation cost, the trajectory of the AUV is divided into various submaps based on Differences of Normals (DoN). Loop closures between submaps are obtained by terrain matching; meanwhile, maximum likelihood terrain estimation is also introduced to build weak data association within the submap. Assisted by one weight voting method for loop closures, the global and local trajectory corrections work together to provide an accurate navigation solution for AUVs with weak data association and inaccurate loop closures. The viability, accuracy and real-time performance of the proposed algorithm are verified with data collected onboard, including an 8 km planned track recorded at a speed of 4 knots in Qingdao, China.

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