Browsing by Author "Shin, Hyo-sang"

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    A review of state-of-the-art 6D pose estimation and its applications in space operations
    (CEAS, 2024-06-11) Singh, Siddharth; Shin, Hyo-sang; Tsourdos, Antonios; Felicetti, Leonard
    Increase in autonomous systems now requires for these systems to work in close proximity of other objects in their environments, with many tasks that need to be done on environment objects for eg., assembly, transportation, rendezvous, docking, or to avoid them like collision detections/avoidance, path planning etc. In this literature review we discuss machine learning based algorithms that solve the first step of vision-based autonomous systems i.e., vision based pose estimation. This paper presents a critical review in advancements of 6D pose estimation using both 2D and 3D input data, and compare how they deal with the challenges shared by the computer-vision based localisation problem. We also look over algorithms with their applications in space based tasks like in-orbit docking, rendezvous and the challenges that come with space-vision applications. To conclude the review we also highlight niche problems and possible avenues for future research.
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    Mission planning for a multiple-UAV patrol system in an obstructed airport environment
    (IEEE, 2023-11-10) Liu, Ruifan; Shin, Hyo-sang; Tsourdos, Antonios
    This paper investigates using multiple unmanned aerial vehicles (UAVs) to carry out routine patrolling at an airport to enhance its perimeter security. It specifically focuses on mission planning of the system to facilitate efficient patrolling with consideration of local buildings and restricted airspace. The proposed methodology includes three aspects: 1) a vision-based set cover algorithm to construct the patrolling network, 2) an obstructed partitioning-based clustering algorithm for recharging station placement, and 3) a mixture integer quadratic programming (MIQP) algorithm to plan routes for UAVs minimizing the maximum idle time through out all patrolling waypoints. The main contribution of this work is that it provides a comprehensive mission planning solution for UAVs persistently patrolling in a complex environment characterized by blocked vision and restricted airspace. The proposed methodology is evaluated through intensive simulations in the context of the Cranfield Airport scenario.