School of Aerospace, Transport and Manufacturing (SATM)

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Browsing School of Aerospace, Transport and Manufacturing (SATM) by Subject "14 Life Below Water"

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    A COLREGs compliance reinforcement learning approach for USV manoeuvring in track-following and collision avoidance problems
    (Elsevier, 2025-01-15) Sonntag, Valentin; Perrusquía, Adolfo; Tsourdos, Antonios; Guo, Weisi
    The development of new technologies for autonomous platforms has allowed their integration into sea mine countermeasures. This has allowed to remove the personnel from the potential danger by having the mine search task performed by an unmanned surface vessel (USV). Traditional intelligent systems are built by agglomerating hand-coded behaviours that determine how a good manoeuvre looks like. This induces cognitive bias into the pre-defined behaviours that can violate safety and regulatory rules imposed by the COLREGs. To alleviate this issue, this paper proposes a COLREGs compliant reinforcement learning (RL) approach that gives a solution for the autonomous navigation of USVs. A custom simulation environment is developed. The RL agents are trained to deal with path-following problem with obstacle avoidance capabilities. A custom reward function is defined to consider the turning disks for the agent's decision process. A smoothing decision feature is used to smooth the transitions between consecutive actions. The results demonstrate good convergence and high performance under different scenarios. The collision avoidance with COLREGs compliances shows the effectiveness of the proposed approach under several scenarios with static and moving obstacles.
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    ItemOpen Access
    PRobust: a percolation-based robustness optimization model for underwater acoustic sensor networks
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-12-31) Zhang, Zhaowei; Liu, Chunfeng; Qu, Wenyu; Zhao, Zhao; Guo, Weisi
    In Underwater Acoustic Sensor Networks (UASNs), the robustness of network is greatly affected by complex marine environments when implementing multi-hop data transmission. Factors such as the underwater acoustic channel and dynamic topological changes induced by multi-layered oceanic vortices exacerbate this influence. However, there is currently a research gap in the specific area of robustness optimization for UASNs. Existing studies on robustness optimization are unsuitable for UASNs as they neglect the considerations of the marine environment and node characteristics (e.g., residual energy). In this work, we propose PRobust, a percolation-based robustness optimization model for UASNs. PRobust consists of two distinct phases: percolation modeling and bottleneck optimization. In the percolation modeling phase, we incorporate both node and edge features, considering the physical and topological properties, and introduce a novel approach for calculating link quality. In the bottleneck optimization phase, we devise a graph theory-based method to identify bottlenecks, leveraging the flow information recorded by nodes to improve the accuracy of bottleneck discovery. Moreover, we integrated time slots and a current movement model into the proposed model, allowing its applicability to dynamically changing UASNs. Extensive simulation results indicate that, compared to existing methods, PRobust significantly enhances network robustness and performance with the same overhead after bottleneck optimization.