scholarly journals Simulation model for energy consumption and acoustic underwater communication of autonomous underwater vehicles

Author(s):  
Peter Danielis ◽  
Helge Parzyjegla ◽  
Mostafa Assem Mohamed Ali ◽  
Frank Sill Torres

AbstractRecently, cooperative autonomous underwater vehicles (AUVs) have been deployed in application areas such as surveillance and protection of maritime infrastructures for inspection and monitoring purposes. These cooperative methodologies require wireless transmission of data between the different AUVs operating in the underwater environment. Communication over ranges exceeding 100 m exclusively relies on underwater acoustic communication. However, the propagating acoustic waves suffer from several challenges due to the presence of path loss, multi-path propagation, the slow and variant propagation speed, background noise, and Doppler distortion. Since the power supply of the AUVs is limited, communication must be very energy efficient and energy constraints have to be known to be able to plan the mission of AUVs. Due to the difficulties of real experiments, the modeling and simulation of the energy consumption and underwater acoustic communication play an essential role in studying and developing these systems. We provide a modular simulation model for the energy consumption and acoustic underwater communication of AUVs implemented in the network simulator OMNeT++ using the INET framework. More specifically, we extend several INET modules in such a way as to reflect the characteristics of AUVs and underwater communication. We study and analyze the AUVs’ energy consumption and dependence of the message quality on different properties such as those mentioned above.

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shingo Yoshizawa ◽  
Takashi Saito ◽  
Yusaku Mabuchi ◽  
Tomoya Tsukui ◽  
Shinichi Sawada

Reliable underwater acoustic communication is demanded for autonomous underwater vehicles (AUVs) and remotely operated underwater vehicles (ROVs). Orthogonal frequency-division multiplexing (OFDM) is robust with multipath interference; however, it is sensitive to Doppler. Doppler compensation is given by two-step processing of resampling and residual carrier frequency offset (CFO) compensation. This paper describes the improvement of a resampling technique. The conventional method assumes a constant Doppler shift during a communication frame. It cannot cope with Doppler fluctuation, where relative speeds between transmitter and receiver units are fluctuating. We propose a parallel resampling technique that a resampling range is extended by measured Doppler standard deviation. The effectiveness of parallel resampling has been confirmed in the communication experiment. The proposed method shows better performance in bit error rates (BERs) and frame error rates (FERs) compared with the conventional method.


2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Yueyue Deng ◽  
Pierre-Philippe J. Beaujean ◽  
Edgar An ◽  
Edward Carlson

Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV) missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic communication conditions: a location-aided task allocation framework (LAAF) algorithm for multitarget task assignment and the grid-based multiobjective optimal programming (GMOOP) mathematical model for finding an optimal vehicle command decision given a set of objectives and constraints. Both the LAAF and GMOOP algorithms are well suited in poor acoustic network condition and dynamic environment. Our research is based on an existing mobile ad hoc network underwater acoustic simulator and blind flooding routing protocol. Simulation results demonstrate that the location-aided auction strategy performs significantly better than the well-accepted auction algorithm developed by Bertsekas in terms of task-allocation time and network bandwidth consumption. We also demonstrate that the GMOOP path-planning technique provides an efficient method for executing multiobjective tasks by cooperative agents with limited communication capabilities. This is in contrast to existing multiobjective action selection methods that are limited to networks where constant, reliable communication is assumed to be available.


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