A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions

Yel, Esen; Bezzo, Nicola

doi:10.1109/iros51168.2021.9635918

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Researchers proposed meta‐learning training to enable UAVs to switch appropriate models in the adaptive environment, to overcome the limitations of fixed models on the adaptability of different inspection environments (Yel & Bezzo, 2021). While the UAV is in operation, the monitoring verification technology determines when the system needs to adjust its model by considering the data‐pruning process of effective learning.…”

Section: Path Planning For Uav Inspectionmentioning

confidence: 99%

Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

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Section: Path Planning For Uav Inspectionmentioning

confidence: 99%

Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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“…A complete active FTC system for quadrotor UAVs has been developed [25], while the BSMC approach and iterative learning algorithm-based FTC are developed in [26]. A meta-learning-based scheme is developed to improve the trajectory tracking performance of UAVs in the presence of the failure in the system and external disturbance [27], and a model-free deep reinforcement learning scheme is applied for a quadrotor with signal rotor failure in [28]. A hierarchical FTC is designed for a hexacopter, where an adaptive sliding mode controller is used for normal operation, and a slidingmode-based controller is used for rotor failure [29].…”

Section: Introductionmentioning

confidence: 99%

Rotor Failure Compensation in a Biplane Quadrotor Based on Virtual Deflection

Dalwadi

Deb

Ožana

2022

Drones

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A biplane quadrotor is a hybrid type of UAV that has wide applications such as payload pickup and delivery, surveillance, etc. This simulation study mainly focuses on handling the total rotor failure, and for that, we propose a control architecture that does not only handle rotor failure but is also able to navigate the biplane quadrotor to a safe place for landing. In this structure, after the detection of total rotor failure, the biplane quadrotor will imitate reallocating control signals and then perform the transition maneuver and switch to the fixed-wing mode; control signals are also reallocated. A synthetic jet actuator (SJA) is used as the redundancy that generates the desired virtual deflection to control the pitch angle, while other states are taken care of by the three rotors. The SJA has parametric nonlinearity, and to handle it, an inverse adaptive compensation scheme is applied and a closed-loop stability analysis is performed based on the Lyapunov method for the pitch subsystem. The effectiveness of the proposed control structure is validated using numerical simulation carried out in the MATLAB Simulink.

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