Review of AUV Underwater Terrain Matching Navigation

Chen, Pengyun; Li, Ye; Su, Yumin; Chen, Xiaolong; Jiang, Yanqing

doi:10.1017/s0373463315000429

Cited by 51 publications

(29 citation statements)

References 29 publications

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“…The main global navigational satellite system (GNSS) positioning method for submersible vehicles is limited to situations where the submersible vehicle can raise an antenna above the surface of the water. However, some AUVs need the more independent method of comparative (terrain) navigation via digital terrain models (DTMs) [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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Autonomous navigation is an important task for unmanned vehicles operating both on the surface and underwater. A sophisticated solution for autonomous non-global navigational satellite system navigation is comparative (terrain reference) navigation. We present a method for fast processing of 3D multibeam sonar data to make depth area comparable with depth areas from bathymetric electronic navigational charts as source maps during comparative navigation. Recording the bottom of a channel, river, or lake with a 3D multibeam sonar data produces a large number of measuring points. A big dataset from 3D multibeam sonar is reduced in steps in almost real time. Usually, the whole data set from the results of a multibeam echo sounder results are processed. In this work, new methodology for processing of 3D multibeam sonar big data is proposed. This new method is based on the stepwise processing of the dataset with 3D models and isoline maps generation. For faster products generation we used the optimum dataset method which has been modified for the purposes of bathymetric data processing. The approach enables detailed examination of the bottom of bodies of water and makes it possible to capture major changes. In addition, the method can detect objects on the bottom, which should be eliminated during the construction of the 3D model. We create and combine partial 3D models based on reduced sets to inspect the bottom of water reservoirs in detail. Analyses were conducted for original and reduced datasets. For both cases, 3D models were generated in variants with and without overlays between them. Tests show, that models generated from reduced dataset are more useful, due to the fact, that there are significant elements of the measured area that become much more visible, and they can be used in comparative navigation. In fragmentary processing of the data, the aspect of present or lack of the overlay between generated models did not relevantly influence the accuracy of its height, however, the time of models generation was shorter for variants without overlay.

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Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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“…where, l x and l y representation in the x and y direction, respectively. K is a quantity related to confidence ε with ε = 0.03, λ 1 , λ 2 , θ defined in Equation (17), γ denotes the square amplification factor of the search interval, and K can be calculated by Equation (21). This initialization method ignores the DR positioning information at the initial TAN time and considers the probability of each point P ij in the search interval, obtained by the estimated navigation error, is equal to Equation (22).…”

Section: B Comparison With Other Initialization Schemesmentioning

confidence: 99%

Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Wang

et al. 2020

IEEE Access

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AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.

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“…AUV development involves high technologies, such as mechanics, fluid mechanics, hydroacoustics, optics, electronic communication, navigation, automatic control, computer science, sensor technology, bionics, artificial intelligence, and many other contemporary achievements. The important application value of AUVs has received an increasing amount of attention by scientists and technologists in the civil and military fields, and in-depth research has been conducted [5], [6]. In the field of marine engineering, AUVs are employed for the structural inspection of dams, installation/disassembly of underwater bases, observation of underwater targets, and search and rescue; they also aid divers.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

138

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An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

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Review of AUV Underwater Terrain Matching Navigation

Cited by 51 publications

References 29 publications

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

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