Solving Gravity Anomaly Matching Problem Under Large Initial Errors in Gravity Aided Navigation by Using an Affine Transformation Based Artificial Bee Colony Algorithm

Dai, Tian; Liu, Miao; Shao, Haijun; Shi, Yongsheng

doi:10.3389/fnbot.2019.00019

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…Field experiments have verified that robots are able to exchange large volumes of data in a short time by visiting a communication node. It is worth mentioning that, as the positions of communication nodes are fixed, an underwater robot can be navigated to a node by affordable methods, such as TRN [23], DBRN [24] and GAN [25].…”

Section: Related Workmentioning

confidence: 99%

“…The network consists of a set of underwater communication nodes. There are various underwater navigation methods—such as Terrain-Referenced Navigation (TRN) [23], Database-Referenced Navigation (DBRN) [24] and Gravity Aided Navigation (GAN) [25]—for an underwater robot to periodically visit the nodes to exchange information and charge batteries if needed.We apply a pheromone-based controller to coordinate a swarm to monitor marine environment and search for static targets on the seafloor. The controller is composed of two layers: the layer of virtual pheromone and the layer of behavior laws.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots

Chen

Geng

et al. 2019

Sensors

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The advent of the swarm makes it feasible to dynamically monitor a wide area for maritime applications. The crucial problems of underwater swarm monitoring are communication and behavior coordination. To tackle these problems, we propose a wide area monitoring strategy that searches for static targets of interest simultaneously. Traditionally, an underwater robot adopts either acoustic communication or optical communication. However, the former is low in bandwidth and the latter is short in communication range. Our strategy coordinates underwater robots through indirect communication, which is inspired by social insects that exchange information by pheromone. The indirect communication is established with the help of a set of underwater communication nodes. We adopt a virtual pheromone-based controller and provide a set of rules to integrate the area of interest into the pheromone. Based on the information in the virtual pheromone, behavior laws are developed to guide the swarm to monitor and search with nearby information. In addition, a robot can improve its performance when using additional far-away pheromone information. The monitoring strategy is further improved by adopting a swarm evolution scheme which automatically adjusts the visiting period. Experimental results show that our strategy is superior to the random strategy in most cases.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots

Chen

Geng

et al. 2019

Sensors

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“…However, this searching mechanism requires a large amount of computation costs and is easy to lead to a low matching efficiency of the algorithms [18]. In addition, the TERCOM also has other shortcomings, including the difficulty in effectively dealing with the observation noise and the process noise [12] and the sensibility of the angle error in the INS range [19]. Therefore, it is necessary to further discuss and research the design problem of grid topology different from that of the TERCOM.…”

Section: Introductionmentioning

confidence: 99%

Improving Matching Accuracy of Underwater Gravity Matching Navigation Based on Iterative Optimal Annulus Point Method with a Novel Grid Topology

Zhao

Zheng

et al. 2021

Remote Sensing

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In this study, we improve the matching accuracy of underwater gravity matching navigation. Firstly, the Iterative Optimal Annulus Point (IOAP) method with a novel grid topology is proposed for breaking through the inherent grid structure limit of the canonical gravity matching algorithm and enhancing its underwater gravity matching accuracy. The theory of IOAP is as follows: (1) small-annulus matching and positioning mechanism on the tracking starting point is developed by employing the starting point and drift error of the INS (Inertial Navigation System), the fixed rotation angle, etc. The optimal matching location of the starting point is obtained by matching and comparing the matched points in this small-annulus grid, which contributes to heightening the initial-position error insensitivity of the algorithms. (2) Variable-angle three-layer annulus matching and positioning mechanisms on the tracking ending point were constructed by using the optimal matching location of the starting point and combining the tracking direction-and-distance information of the INS and the cumulative drift error, etc. It is used to generate the annulus matching points with the ring-type grid topology. (3) The optimal matching position of the ending point in this annulus is obtained by iteratively calculating the evaluation index value of the matching points and following the evaluation index optimal rule. Secondly, we comprehensively consider the main performance evaluation indexes of the underwater gravity matching algorithms, such as the statistical indicators of the matching accuracy, the average matching time and the matching success rate, and take them as a basis of the pros and cons of the matching analysis. Furthermore, under conditions that include different scale searching regions or different reference-angle ring radii, the statistical results verify that the IOAP had a different matching ability and better robustness. Finally, several trajectories with the starting points from different areas and the ending points in different gravity ranges are tested and compared to carry out the numerical simulations. These results indicate that the IOAP has many advantages, such as a high matching accuracy and strong positioning applicability in different gravity regions. Compared with the TERCOM (terrain contour matching algorithm), its average matching accuracy was the highest, increased by 40.39%.

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“…Because the inertial navigation system (INS) is prone to accumulate errors over time, underwater vehicles need to use other physical information combined with INS for positioning navigation. The gravity-aided inertial navigation system (GAINS) can match the real-time gravity information with the pre-stored marine gravity anomaly map to obtain the accurate position of the underwater vehicle and correct the accumulated error of the INS [1][2][3][4][5][6][7]. The GAINS does not radiate energy or accept electromagnetic signals when obtaining gravity information.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

Wang

Zheng

2021

Remote Sensing

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This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter–support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter–support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.

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Solving Gravity Anomaly Matching Problem Under Large Initial Errors in Gravity Aided Navigation by Using an Affine Transformation Based Artificial Bee Colony Algorithm

Cited by 10 publications

References 37 publications

A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots

A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots

Improving Matching Accuracy of Underwater Gravity Matching Navigation Based on Iterative Optimal Annulus Point Method with a Novel Grid Topology

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

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