Determination of the Position Using Receivers Installed in UAV

Świerczyński, Sławomir; Felski, Andrzej

doi:10.1109/euronav.2019.8714161

Cited by 7 publications

(5 citation statements)

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“…Upon receipt of a quarry photo location, it is then transited to the external control subsystem within CC (CC ECSS). There, this material is subjected to interactive processing [8][9][10][11], the algorithm of which is as follows:…”

Section: Formation Of Spline Frames For Umv Movement Routesmentioning

confidence: 99%

Developing the concept of autonomous control of the quarry vehicles movement

et al. 2021

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Introduction. The article presents the results of studies related to the description of the synthesis and further analysis of the dynamic behavior of unmanned vehicles (UMVs) operating at coal opencast mines and other mining enterprises. The relevance of the research topic is dictated by the need to create safe and effective conditions for overburden / mining and mining transportation operations. This is ensured, among other things, by the operation of unmanned heavy-duty dump trucks. Purpose of the work: in the course of the research, a concept was developed for the formation of route parameters and models of the dynamics of movement of UMVs along technological routes. Methodology. The paper describes the technology for generating and processing signals in control subsystems as part of an automated dispatching system and an on-board subsystem for autonomous control of the UMV. Research methods include the study of the shapes and characteristics of current trajectories (CT) in relation to a nominal axial trajectory (NAT). In this case, a current trajectory of the UMV is considered within the framework of the so-called S-frames, which are formed as geolocations on a certain working UMV route from a bench face to the place of unloading and vice versa. As a variable characterizing the UMV behavior on a current trajectory, a 1D-signal with a time dependent frequency is adopted, the nature of the change of which is determined by the CT dynamic shape relative to the NAT. Results. Such chirp signals – together with the wavelet transforms technique introduced into consideration – allow us in a semantically transparent and information-rich videographic form to display and subject to further processing signals characterizing the current behavior of a UMV on a route. The resulting parameters of such secondary wavelet representations of the current UMV dynamics (wavelet maps) are further planned to be used in the subsystem of dynamic modal control of the UMV movement in the open pit. Conclusions. The results of the studies carried out make it possible to form current routes of UMV movement according to the models generated based on the analytical tools accompanying the dynamics of the UMVs, proposed in the work.

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Section: Formation Of Spline Frames For Umv Movement Routesmentioning

confidence: 99%

Developing the concept of autonomous control of the quarry vehicles movement

et al. 2021

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“…As previously mentioned, performance limitations have an impact on the manoeuvrability of the ownship aircraft, which in turn limits the range of actions that can be performed to avoid a conflict. For unmanned aviation, this work employs the DJI Mavic Pro, a well-known model used in a wide range of applications [134][135][136]. However, a mission employing an UAS model with significant differences in performance (e.g., a fixed-wing model), should not directly extrapolate from the results herein obtained.…”

Section: Apparatus and Aircraft Modelsmentioning

confidence: 99%

Review of Conflict Resolution Methods for Manned and Unmanned Aviation

2020

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Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in Conflict Detection and Resolution (CD&R) methods. With the new applications of drones, and the implications of a profoundly different urban airspace, new demands are placed on such algorithms, further spurring new research. This paper presents a review of current CR methods for both manned and unmanned aviation. It presents a taxonomy that categorises algorithms in terms of their approach to avoidance planning, surveillance, control, trajectory propagation, predictability assumption, resolution manoeuvre, multi-actor conflict resolution, considered obstacle types, optimization, and method category. More than a hundred CR methods were considered, showing how most work on a tactical, distributed framework. To enable a reliable comparison between methods, this paper argues that an open and ideally common simulation platform, common test scenarios, and common metrics are required. This paper presents an overview of four CR algorithms, each representing a commonly used CR algorithm category. Both manned and unmanned scenarios were tested, through fast-time simulations on an open-source airspace simulation platform.

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“…The algorithm proposed in this paper is applied to the positioning system of real UAV, and experiments are carried out in relatively open outdoor scenes. Since DJI's fused data is relatively accurate [35], the GPS data of DJI's M100 drone is used as the comparison data of the positioning data. The experimental trajectory comparison chart is shown in Fig.…”

Section: A Simulationmentioning

confidence: 99%

UAV Positioning Based on Multi-Sensor Fusion

et al. 2020

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Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability. INDEX TERMS Multi-sensor fusion, unmanned aerial vehicle, positioning, adaptive Kalman filtering.

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Determination of the Position Using Receivers Installed in UAV

Cited by 7 publications

References 0 publications

Developing the concept of autonomous control of the quarry vehicles movement

Developing the concept of autonomous control of the quarry vehicles movement

Review of Conflict Resolution Methods for Manned and Unmanned Aviation

UAV Positioning Based on Multi-Sensor Fusion

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