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Matthew Garratt

Researcher at University of New South Wales

Publications -  234
Citations -  3015

Matthew Garratt is an academic researcher from University of New South Wales. The author has contributed to research in topics: Control theory & Fuzzy logic. The author has an hindex of 21, co-authored 213 publications receiving 2188 citations. Previous affiliations of Matthew Garratt include Australian National University & Australian Defence Force Academy.

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Approaches for a tether-guided landing of an autonomous helicopter

TL;DR: The design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea, is addressed and the proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability.
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Monocular vision-based real-time target recognition and tracking for autonomously landing an UAV in a cluttered shipboard environment

TL;DR: Experiments show that the vision system is accurate, robust, and capable of dealing with an incomplete landing target, whilst the overall implementation shows the practicability of real-time onboard target tracking and closed-loop control.
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Flight control of a rotary wing UAV using backstepping

TL;DR: In this paper, a back-stepping-based controller for autonomous landing of a rotary wing UAV (RUAV) is presented, which holds good for the full flight envelope control, is an extension of a back stepping algorithm for general rigid body velocity control.
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State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles

TL;DR: Many aspects of the developments and implementations of soft computing techniques in aerial robotics with the main focus on its flight control systems are discussed, including evolutionary autopilots for small unmanned aerial vehicles (UAVs).
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Visual–Inertial Navigation Systems for Aerial Robotics: Sensor Fusion and Technology

TL;DR: This paper comprehensively discusses the current progress of visual–inertial (VI) navigation systems and sensor fusion research with a particular focus on small unmanned aerial vehicles, known as microaerial vehicles (MAVs).