Cooperative Search and Survey Using Autonomous Underwater Vehicles (AUVs)

Yoon, Seokhoon; Qiao, Chunming

doi:10.1109/tpds.2010.88

Cited by 105 publications

(44 citation statements)

References 41 publications

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“…The target has already found the hunting AUVs moving towards it and it starts to escape from its initial location. Figure 6(c) shows that in the final state of the hunting process, the moving target is surrounded by hunting AUVs in (11,9) and, through the hunting task, it is easy to see that the AUVs are moving directly to the target and do not collide with each other. Table 1 lists the activity value of the neuron at each step in the hunting process of AUV P C 3 under the circumstance with obstacles.…”

Section: Hunting Simulation Experiments Without Obstaclesmentioning

confidence: 99%

Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments

Zhu

Cao

et al. 2015

International Journal of Advanced Robotic Systems

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The multi-AUV hunting problem is one of the key issues in multi-robot system research. In order to hunt the target efficiently, a new hunting algorithm based on a bioinspired neural network has been proposed in this paper. Firstly, the AUV's working environment can be represented, based on the biological-inspired neural network model. There is one-to-one correspondence between each neuron in the neural network and the position of the grid map in the underwater environment. The activity values of biological neurons then guide the AUV's sailing path and finally the target is surrounded by AUVs. In addition, a method called negotiation is used to solve the AUV's allocation of hunting points. The simulation results show that the algorithm used in the paper can provide rapid and highly efficient path planning in the unknown environment with obstacles and non-obstacles.

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Section: Hunting Simulation Experiments Without Obstaclesmentioning

confidence: 99%

Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments

Zhu

Cao

et al. 2015

International Journal of Advanced Robotic Systems

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“…It is worth noting that several works have been done on multiple AUVs for ocean sampling [3]. Slocum Gliders were used for cooperative control for ocean sampling in [5].…”

Section: Related Workmentioning

confidence: 99%

“…Gliders periodically send data and receive control signals from onshore station. However, a new mechanism is developed to use multiple AUVs in a large network [3]. Survey region is divided into 'n' lanes.…”

Section: Related Workmentioning

confidence: 99%

“…However, synchronization with AUV is highly required, energy depletion of gateway node is unavoidable and communication time is required for data exchange between sensor node and AUV. Various alternative data collection algorithms have been proposed in [3,4], but the problem of reliable data delivery and energy efficiency is still left open.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aeerp

Ahmad

Wahid²,

Kim³

2013

Proceedings of the 8th ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks

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Multi-hop communication in Underwater AcousticSensor Network (UASN) brings new challenges in reliable data transmission. Recent work shows that data collection from underwater sensor nodes using Autonomous Underwater Vehicle (AUV) minimizes multi-hop data transmission, improving energy efficiency in UASN. However, due to continuous movements, AUV has limited communication time to collect data from sensor nodes in UASN. We therefore propose a novel underwater routing protocol, namely AEERP (AUV aided Energy Efficient Routing Protocol). In AEERP, an AUV collects data from gateway nodes. Among all the nodes, selected nodes play the role of gateway node for communication with AUV. Gateway nodes are selected based on RSSI values of the hello packet received from AUV. In addition, after consuming energy up to a certain threshold by a gateway node, another high energy node is selected as a gateway node. This adaptive selection of gateway node balances energy consumption increasing network lifetime. Mathematical analysis and NS-2 simulation show that AEERP performs better than Travel Salesperson Problem (TSP) in terms of energy cost and data delivery ratio.

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“…Further, owing to the constraints of the on-board energy resources, the operational time of a mobile node is limited, consequently reducing the coverage area. The operational problems faced by a mobile node can be addressed if multiple mobile nodes are employed, where each mobile traverses the network on a distinct trajectory [11,12,13,14,15,16]. Then, these mobile nodes may coordinate with each other to remain in constant interconnection, enabling multihop communications over the coverage area.…”

Section: Introductionmentioning

confidence: 99%

Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach

Khan¹,

Cho²

2016

Sensors

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In this paper, we propose a data-gathering scheme for hierarchical underwater sensor networks, where multiple Autonomous Underwater Vehicles (AUVs) are deployed over large-scale coverage areas. The deployed AUVs constitute an intermittently connected multihop network through inter-AUV synchronization (in this paper, synchronization means an interconnection between nodes for communication) for forwarding data to the designated sink. In such a scenario, the performance of the multihop communication depends upon the synchronization among the vehicles. The mobility parameters of the vehicles vary continuously because of the constantly changing underwater currents. The variations in the AUV mobility parameters reduce the inter-AUV synchronization frequency contributing to delays in the multihop communication. The proposed scheme improves the AUV synchronization frequency by permitting neighboring AUVs to share their status information via a pre-selected node called an agent-node at the static layer of the network. We evaluate the proposed scheme in terms of the AUV synchronization frequency, vertical delay (node→AUV), horizontal delay (AUV→AUV), end-to-end delay, and the packet loss ratio. Simulation results show that the proposed scheme significantly reduces the aforementioned delays without the synchronization time-out process employed in conventional works.

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Cooperative Search and Survey Using Autonomous Underwater Vehicles (AUVs)

Cited by 105 publications

References 41 publications

Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments

Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments

Aeerp

Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach

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