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are critical especially around congested or critical infrastructures. This research aims to develop decision making and planning algorithms that can mitigate the risks challenging environments of AAM
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thesis shall highlight the proposed risk embedding technique into MASs path planning algorithms, enabling them to realise guaranteed, conservative, yet risk-feasible trajectories for efficient state-space
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from such machines to derive algorithms expressing their state of health and next maintenance needs. A background in both engineering and machine learning would be useful, although help is readily
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