Neural network for black-box fusion of underwater robot localization under unmodeled noise

Chame, Hendry Ferreira; Santos, Matheus Machado dos; Botelho, Silvia Silva da Costa

doi:10.1016/j.robot.2018.08.013

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…An algorithm that solves it has been proposed in [134], [135] where a vehicle builds in a real-time fashion and updates the map during the exploration, merging measurement data from proprioceptive sensors and from a 1D laser line to replicate the profiling sonar. Localization using neural networks was also achieved in [136]. Simulators are handy for comparing different localization techniques starting from the same datasets.…”

Section: B Uuv Simulatormentioning

confidence: 99%

Underwater Simulators Analysis for Digital Twinning

Ciuccoli,

Screpanti,

Scaradozzi

2024

IEEE Access

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The underwater environment is among Earth's most challenging domains, where failures incur elevated risks and costs for both technology and human endeavors. As a consequence, forecasting the behavior of mechatronic systems through simulation has become increasingly important. Underwater Robotic Simulators (URSs) allow researchers and engineers to safely develop and assess submarine systems. The selection of an appropriate URS from the list of available tools is not trivial. Moreover, the integration of this software with the Digital Twin (DT) concept presents numerous advantages, particularly the ability to link the simulated environment with actual underwater vehicles. This connection is facilitated by performing validation and simulation tests using Software In the Loop (SIL), Model In the Loop, and Hardware In the Loop (HIL) techniques. This paper extensively reviews URSs in the context of both robots and unmanned vehicles in light of the DT paradigm. The article critically examines distinctions among existing URSs, offering valuable insights to aid researchers in selecting the most fitting tool for their specific applications. Additionally, the review explores the practical applications of the identified simulators, categorizing their usage across different fields to illuminate the preferences within the scientific community and showcase prominent case studies.

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Section: B Uuv Simulatormentioning

confidence: 99%

Underwater Simulators Analysis for Digital Twinning

Ciuccoli,

Screpanti,

Scaradozzi

2024

IEEE Access

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“…In this way, the nodes are spread from the high-density area to the low-density area to form a uniform distribution, and the coverage and connectivity of the network are greatly improved. Different from the above article, the need for uniform coverage of monitoring areas, the literature [23] studied the deployment of underwater sensor nodes under event-driven nonuniform coverage requirements. e events here refer to various dynamic and static targets of interest to users.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, the above deployment methods are simulated and verified, and the performance of the virtual force-based redeployment algorithm in this chapter is compared with the FSSD algorithm in [23] under the same simulation conditions.…”

Section: Simulation Analysesmentioning

confidence: 99%

Event-Sensitive Network: A Construction Algorithm of Agricultural Sensor Network Driven by Environmental Change

Liu

et al. 2021

Mathematical Problems in Engineering

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In a wireless sensor network, the sensor nodes transmit the acquired information to the server through the data transmission link. On the serverside, the data are processed, fused, and expressed to serve the user. Sensor deployment is a key factor related to the stability and security of wireless networks. This article uses environmental changes to drive related technologies to deploy wireless sensors. In this article, environmental change-driven means that through certain deployment cost model assumptions and problem descriptions, network deployment is artificially divided into two stages: initial deployment and redeployment. In the deployment phase, by referring to the idea of virtual force, a new sensor deployment algorithm is proposed in the redeployment phase, which can well solve the stability- and security-related issues encountered in agricultural wireless sensor networks. In this algorithm, the moving distance of the mobile receiver and the average coverage of the network are calculated based on the virtual force, the direction, and the number of adjacent clusters. Finally, the algorithm model was simulated in MATLAB, and the feasibility of the algorithm was verified by analyzing the event coverage and the moving distance of nodes. The final simulation results show that the algorithm proposed in this paper can achieve better performance than existing algorithms in terms of average coverage and moving distance.

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“…For underwater robots important uses of artificial neural networks are navigation and control. Current examples are the localisation of an underwater robot considering unmodelled noise [32] and the target tracking of underactuated AUVs [33].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Determination of Flow Parameters of a Water Flow Around an AUV Body

Hoth

Kowalczyk

2019

Robotics

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Autonomous underwater vehicles (AUVs) have changed the way marine environment is surveyed, monitored and mapped. Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings. AUVs not only perform a given task but also adapt to changes in the environment, e.g., sudden side currents, downdrafts, and other effects which are extremely unpredictable. To navigate properly and allow simultaneous localisation and mapping (SLAM) algorithms to be used, these effects need to be detected. With current navigation systems, these disturbances in the water flow are not measured directly. Only the indirect effects are observed. It is proposed to detect the disturbances directly by placing pressure sensors on the surface of the AUV and processing the pressure data obtained. Within this study, the applicability of different learning methods for determining flow parameters of a surrounding fluid from pressure on an AUV body are tested. This is based on CFD simulations using pressure data from specified points on the surface of the AUV. It is shown that support vector machines are most suitable for the given task and yield excellent results.

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Neural network for black-box fusion of underwater robot localization under unmodeled noise

Cited by 12 publications

References 16 publications

Underwater Simulators Analysis for Digital Twinning

Underwater Simulators Analysis for Digital Twinning

Event-Sensitive Network: A Construction Algorithm of Agricultural Sensor Network Driven by Environmental Change

Determination of Flow Parameters of a Water Flow Around an AUV Body

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