Fig 6
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The development of Unmanned Aerial Systems (UASs) continuously improves and advances the technology, which is a key enabler for many new applications, such as autonomous Beyond Visual Line of Sight (BVLOS) operations. However, ensuring a sufficient level of safety and performance of an UAS can be a challenging task, since it requires systematic exp...
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Context 1
... experimental setup used in this study is shown on Figure 6a, where the multirotor sUAS (Figure 6b) is hovering in the center of the detection volume. An overview of the sUAS's technical and flight specifications is shown in Table I. ...
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Citations
... In [8], a framework was developed using a motion capture system, Wi-Fi and ROS in order to carry out endurance tests and detect abnormal behavior in the signals sent to the motors. In this study, experiments were carried out with various flight patterns, where a fault detection feature was implemented to activate an automatic landing as soon as the UAV deviates from its path by 20 or 30 centimeters. ...
... Compared with the literature relying on GPS information [9,36,37] or MCS [38][39][40] for positioning, the method in this study uses only a range sensor and its odometer to achieve navigation and positioning at a minimum deployment cost. Compared with relative positioning based on visual search, the method here has wider-range measurements, shorter searching times and requires fewer computing resources. ...
This study designs a navigation and landing scheme for an unmanned aerial vehicle (UAV) to autonomously land on an arbitrarily moving unmanned ground vehicle (UGV) in GPS-denied environments based on vision, ultra-wideband (UWB) and system information. In the approaching phase, an effective multi-innovation forgetting gradient (MIFG) algorithm is proposed to estimate the position of the UAV relative to the target using historical data (estimated distance and relative displacement measurements). Using these estimates, a saturated proportional navigation controller is developed, by which the UAV can approach the target, making the UGV enter the field of view (FOV) of the camera deployed in the UAV. Then, a sensor fusion estimation algorithm based on an extended Kalman filter (EKF) is proposed to achieve accurate landing. Finally, a numerical example and a real experiment are used to support the theoretical results.
... The results suggest that extreme learning neuro-fuzzy algorithms based FDI models, especially Fuzzy-ELM and R-EL-678 ANFIS, are promising candidates for developing FDI models for partial actuator faults in multi-rotor UAVs. These FDI 679 models can also be developed to perform control reconfiguration and validated through real-world experimental [70] or 680 hardware-in-the-loop experimental flights [71], [72]. 681 ...
Undetected partial actuator faults on multi-rotor UAVs can lead to system failures and uncontrolled crashes, necessitating the development of accurate and efficient fault detection and isolation (FDI) strategy. This paper proposes a hybrid FDI model for a quadrotor UAV that integrates an extreme learning neuro-fuzzy algorithm with a model-based extended Kalman filter (EKF). Three FDI models using Fuzzy-ELM, R-EL-ANFIS, and EL-ANFIS are compared based on training, validation performances, and sensitivity to weaker and shorter actuator faults. They are also tested online for linear and nonlinear incipient faults by measuring their isolation time delays and accuracies. The results show that the Fuzzy-ELM FDI model exhibits greater efficiency and sensitivity, while Fuzzy-ELM and R-EL-ANFIS FDI models demonstrate better performance than a conventional neuro-fuzzy algorithm, ANFIS.
... As an alternative, Flåten [53] demonstrates how ice formations can be delineated using thermal images from a UAS, and thus provides a proof of concept of the technique. Current sensor technologies need to be integrated, improved, and tested, since the accuracy of the current system is not sufficient [54]. ...
The Arctic is one of the least developed and most under-invested regions in the world, primarily due to its harsh environment and remoteness. However, with the current retreat of Arctic sea ice, the pristine Arctic region is gradually open for economic activities and has been of great interest for Arctic council members. For example, the European Union (EU) Integrated Policy for the Arctic has a clear emphasis on the issues specific to the European Arctic. In particular, it focuses on sustainable Arctic maritime economic growth and launches a process of identifying and developing its relevant key enabling technologies. Unmanned Aircraft System (UAS) or drone is a key enabling technology for missions in harsh and remote Arctic environment, such as finding shorter and safer shipping routes, geological survey for undiscovered rare earth elements, delivering medical supplies to remote areas, Search & Rescue operations, and tracking Arctic ice and vegetation for climate change study. In this paper, we present challenges, opportunities, and enabling technologies related to the development of UAS for Arctic applications. Furthermore, we discuss our hands-on experience of flying drones in harsh Arctic environment and provide a list of operational risks and recommendations.
... • "No probable failure of the UAS or any external system supporting the operation should lead to operation outside the operational volume" [5, p. 30]. Probable UAV failures have been investigated by Jepsen et al. [40]. ...
