Advances in intelligent and autonomous navigation systems for small UAS

Bijjahalli, Suraj; Sabatini, Roberto; Gardi, Alex

doi:10.1016/j.paerosci.2020.100617

Cited by 61 publications

(34 citation statements)

References 115 publications

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“…Nilai Q dan R dapat diubah-ubah untuk menentukan pembobotan atas nilai keluaran beberapa sensor dalam proses penggabungan data sensor atau data fusion sebagaimana Gambar 2. Meski demikian, kebanyakan peneliti memilih adanya perubahan nilai R daripada Q, karena pengubahan nilai Q dapat mengakibatkan adanya overshoot pada nilai Kalman gain [12][13].…”

Section: Gambar 1 Proses DI Kalman Filterunclassified

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Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

Kurniawan¹,

Nasution²,

Dinaryanto³

et al. 2021

Jurnal Teknik Elektro AVITEC

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In an unmanned aircraft vehicle, a navigation system is needed to calculate its orientation and translation. The navigation system can utilize data from the accelerometer, gyroscope, magnetometer, and GPS. The orientation can be precisely calculated from the accelerometer and magnetometer data when the sensor is in a static state. Meanwhile, under dynamic conditions, the orientation can be more precisely calculated from the gyroscope data. In order to obtain the robust navigation system, a data fusion based on Kalman filter is built to calculate the orientation from the accelerometer, gyroscope, and magnetometer. The Kalman filter trusts more in the data from the accelerometer and magnetometer when the UAV is static and trusts more in to the gyroscope data when the UAV is in dynamic conditions. Meanwhile, the UAV translation is obtained by performing data fusion of the accelerometer data with location data from the GPS sensor. The Kalman filter combines data from the accelerometer and GPS when available, otherwise trusts in data from the accelerometer only. The trust level shifting is done by changing the measurement noise covariance. The data fusion based on Kalman filter provides more accurately the orientation and translation data. The orientation as a result of the calculation from the gyroscope has an average error of 18.12%, while the orientation as a result of the accelerometer and magnetometer has an error of 1.3%. By using Kalman filter-based data fusion, the error of the orientation decreases to 0.87%

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Section: Gambar 1 Proses DI Kalman Filterunclassified

“…Sensor akselerometer mengukur percepatan pada sumbu x, y, dan z. Dari nilai percepatan tersebut dapat ditentukan percepatan yang bekerja dalam koordinat navigasi menggunakan Persamaan (13).…”

Section: Penentuan Translasi Uavunclassified

Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

Kurniawan¹,

Nasution²,

Dinaryanto³

et al. 2021

Jurnal Teknik Elektro AVITEC

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“…To provide alternatives to GNSS position fixing, a range of other methods have been developed. These alternatives include terrain referenced navigation (using laser or radar sensors) 31 , camera-based visual map-matching 32,33 , and terrestrial radio signals; which could be navigation specific (e.g. LORAN/e-LORAN 34 ) or standard broadcast signals (often termed 'signals of opportunity') 35 .…”

Section: Introductionmentioning

confidence: 99%

Position fixing with cold atom gravity gradiometers

Phillips,

Wright,

Riou

et al. 2022

Preprint

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This paper proposes a position fixing method for autonomous navigation using partial gravity gradient solutions from cold atom interferometers. Cold atom quantum sensors can provide ultra-precise measurements of inertial quantities, such as acceleration and rotation rates. However, we investigate the use of pairs of cold atom interferometers to measure the local gravity gradient and to provide position information by referencing these measurements against a suitable database. Simulating the motion of a vehicle, we use partial gravity gradient measurements to reduce the positional drift associated with inertial navigation systems. Using standard open source global gravity databases, we show stable navigation solutions for trajectories of over 1000km.

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“…Both of the mentioned techniques were used to estimate the position, velocity, and attitude of vehicle with some success. Another approach is to use a combination of computer vision techniques with deep learning [ 11 , 12 , 13 , 14 ]. According to [ 11 ], vision based systems can be roughly classified into three categories: map-based, map-building, and map-less systems.…”

Section: Introductionmentioning

confidence: 99%

Ground Speed Optical Estimator for Miniature UAV

Chmielewski

Sibilski

2021

Sensors

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In a conventional Unmanned aerial vehicles (UAV) navigational system Global Navigation Satellite System (GNSS) sensor is often a main source of data for trajectory generation. Even video tracking based systems need some GNSS data for proper work. The goal of this study is to develop an optics-based system to estimate the ground speed of the UAV in the case of the GNSS failure, jamming, or unavailability. The proposed approach uses a camera mounted on the fuselage belly of the UAV. We can obtain the ground speed of the airplane by using the digital cropping, the stabilization of the real time image, and template matching algorithms. By combining the ground speed vector components with measurements of airspeed and altitude, the wind velocity and drift are computed. The obtained data were used to improve efficiency of the video-tracking based on a navigational system. An algorithm allows this computation to be performed in real time on board of a UAV. The algorithm was tested in Software-in-the-loop and implemented on the UAV hardware. Its effectiveness has been demonstrated through the experimental test results. The presented work could be useful for upgrading the existing MUAV products (with embedded cameras) already delivered to the customers only by updating their software. It is especially significant in the case when any necessary hardware upgrades would be economically unjustified or even impossible to be carried out.

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Advances in intelligent and autonomous navigation systems for small UAS

Cited by 61 publications

References 115 publications

Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

Position fixing with cold atom gravity gradiometers

Ground Speed Optical Estimator for Miniature UAV

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