Cooperative Autonomous Marine Vehicles for Adaptive Passive Acoustic Monitoring

  • George Rossides

Student thesis: Doctoral ThesisPhD


In the quest for the exploration and study of the vast and complex oceanic environments and the biological species that inhabit them, a variety of different methodologies and techniques are currently employed. Among them, the ability to localise and monitor underwater acoustic sources by passively observing the signals that they emit was proven to be a valuable asset for the study of both biological entities (e.g. marine mammals) as well as anthropogenic ones (e.g. transportation vessels, off-shore drilling processes etc.). Traditional marine environment monitoring systems employ a single oceanographic research vessel, equipped with all the necessary sensors for the acquisition of data. This method of study of marine environments is expensive, time-consuming and unable to study efficiently the large areas that need to be explored. Therefore, recent research turned towards the employment of low-cost, semi-disposable, distributed sensing nodes for simultaneous data collection over large distances. The large numbers of nodes used in such systems, combined with their simple, low-cost, decentralised nature matches the ethos of another engineering field - that of swarm robotics. Therefore, it has recently been proposed that swarm intelligence algorithms and techniques could be used for the control of the movement of such marine systems. This thesis introduces several novel swarm intelligence algorithms for use for the control of marine distributed sensor networks with passive acoustic capabilities. This is achieved through the modification of a prevalent swarm intelligence algorithm, particle swarm optimisation (PSO), originally used in numerical optimisation applications. The first half of the thesis is concerned with adapting PSO for the accurate low-level motion control of robotic swarms with the task of source localisation, while also allowing for the inclusion of alternative swarm robotic tasks like obstacle avoidance and aggregation. Afterwards, the thesis focuses on combining PSO with wavefield correlation techniques, currently used in marine passive acoustic systems such as multi-hydrophone arrays. In the end, it is demonstrated how the proposed algorithms can be combined together, enabling a robotic swarm of marine surface vehicles to localise a marine acoustic source, approach and encircle it and continue monitoring it while maintaining a minimum distance from it. All of the presented algorithms are implemented and tested using MATLAB simulations, while some of them are also further validated using Gazebo simulations employing detailed robot models.
Date of Award16 Nov 2022
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorBenjamin Metcalfe (Supervisor) & Alan Hunter (Supervisor)


  • Swarm Robotics
  • Marine Robotics
  • Passive Acoustics
  • Source Localization

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