Browsing by Author "Sonntag, Valentin"

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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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    Reducing viral transmission through AI-based crowd monitoring and social distancing analysis
    (IEEE, 2022-10-13) Fraser, Benjamin; Copp, Brendan; Singh, Gurpreet; Keyvan, Orhan; Bian, Tongfei; Sonntag, Valentin; Xing, Yang; Guo, Weisi; Tsourdos, Antonios
    This paper explores multi-person pose estimation for reducing the risk of airborne pathogens. The recent COVID-19 pandemic highlights these risks in a globally connected world. We developed several techniques which analyse CCTV inputs for crowd analysis. The framework utilised automated homography from pose feature positions to determine interpersonal distance. It also incorporates mask detection by using pose features for an image classification pipeline. A further model predicts the behaviour of each person by using their estimated pose features. We combine the models to assess transmission risk based on recent scientific literature. A custom dashboard displays a risk density heat-map in real time. This system could improve public space management and reduce transmission in future pandemics. This context agnostic system and has many applications for other crowd monitoring problems.