SLAM-based Underwater Adaptive Sampling Using Autonomous Vehicles

Rahmati, Mehdi; Karten, Seth; Pompili, Dario

doi:10.1109/oceans.2018.8604552

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…SLAM is a widely used technique in ground robots, but less feasible in underwater environment specially in high turbidity situations and in the absence of reliable static landmarks. Some underwater visual SLAM solutions, such as in [13], create a sparse map for the navigation and localization in clear water.…”

Section: Related Workmentioning

confidence: 99%

UW-SVC: Scalable Video Coding Transmission for In-Network Underwater Imagery Analysis

Rahmati

Pompili

2019

2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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Underwater imagery has enabled numerous civilian applications in various domains, ranging from academia to industry, and from industrial surveillance and maintenance to environmental protection and behavior of marine creatures studies. The accumulation of litter and plastic debris at the seafloor and the bottom of rivers are extremely harmful for the aquatic life. We propose a solution for monitoring this problem using a team of Autonomous Underwater Vehicles (AUVs) to exchange the recorded video in order to reconstruct the map of regions of interest. However, underwater video transmission is a challenge in the harsh environment in which radio-frequency waves are absorbed for distances above a few tens of meters, optical waves require narrow laser beams and suffer from scattering and ocean wave motion, and acoustic waves-while long range-provide a very low bandwidth and unreliable channel for communication. In our solution, the scalable coded video of each vehicle is shared in-network with a selected group of receiving vehicles through the underwater acoustic channel. Presented evaluations, including both simulations and experiments, confirm the efficiency and flexibility of the proposed solution using acoustic software-defined modems.

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Section: Related Workmentioning

confidence: 99%

UW-SVC: Scalable Video Coding Transmission for In-Network Underwater Imagery Analysis

Rahmati

Pompili

2019

2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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“…Motivation and Vision: Using one robot to capture the spatial and temporal distributions of the phenomenon with adaptive sampling [6] is subject to many constraints, such as a single point of failure and energy inefficiency [17]. For example, a robot could run out of energy midway during adaptive sampling.…”

Section: Introductionmentioning

confidence: 99%

Uw-Marl

Rahmati

Nadeem

Sadhu

et al. 2019

Proceedings of the International Conference on Underwater Networks &Amp; Systems

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Near-real-time water-quality monitoring in uncertain environments such as rivers, lakes, and water reservoirs of different variables is critical to protect the aquatic life and to prevent further propagation of the potential pollution in the water. In order to measure the physical values in a region of interest, adaptive sampling is helpful as an energy-and time-efficient technique since an exhaustive search of an area is not feasible with a single vehicle. We propose an adaptive sampling algorithm using multiple autonomous vehicles, which are well-trained, as agents, in a Multi-Agent Reinforcement Learning (MARL) framework to make efficient sequence of decisions on the adaptive sampling procedure. The proposed solution is evaluated using experimental data, which is fed into a simulation framework. Experiments were conducted in the Raritan River, Somerset and in Carnegie Lake, Princeton, NJ during July 2019.

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“…Recently, underwater wireless sensor networks (UWSNs) have attracted much attention and provided sufficient technical support to many application fields, such as ocean environment monitoring, natural disaster prevention and military defense [1][2]. Localization technology is one of the key technologies of UWSNs [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…UWSNs localization mainly faces the following difficulties: (1) In UWSNs, the underwater acoustic channel has special characteristics such as narrow bandwidth, high propagation delay, and severe multipath scattering [8][9], which lead to large ranging error and greatly affect the accuracy of the ranging-based localization algorithm. (2) Because the deployment of underwater nodes is large, and the number of beacon nodes is small and the distribution is sparse, the localization algorithm relying on high-density beacon nodes cannot be used. (3) More importantly, all underwater nodes constantly moving by the influence of ocean currents and tides.…”

Section: Introductionmentioning

confidence: 99%

“…solve Eqs(2) and(4), we could get could be solved from Eq. (6), and the z-axis coordinate has been measured by the pressure sensor, so the coordinate of NA is Ranging correction localization Repeating the above process for the beacons B, C, and D, the coordinates of NB, NC, and ND can be obtained, recorded as axis coordinates of node N can be obtained by the centroid algorithm.…”

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confidence: 99%

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A Localization Algorithm for Underwater Wireless Sensor Networks Based on Ranging Correction and Inertial Coordination

Guo¹,

Kang²,

Han³

et al. 2019

KSII TIIS

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Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.

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SLAM-based Underwater Adaptive Sampling Using Autonomous Vehicles

Cited by 12 publications

References 15 publications

UW-SVC: Scalable Video Coding Transmission for In-Network Underwater Imagery Analysis

UW-SVC: Scalable Video Coding Transmission for In-Network Underwater Imagery Analysis

Uw-Marl

A Localization Algorithm for Underwater Wireless Sensor Networks Based on Ranging Correction and Inertial Coordination

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