scholarly journals Enhanced Attitude and Altitude Estimation for Indoor Autonomous UAVs

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 18
Author(s):  
Salvatore Rosario Bassolillo ◽  
Egidio D’Amato ◽  
Immacolata Notaro ◽  
Gennaro Ariante ◽  
Giuseppe Del Core ◽  
...  

In recent years the use of Unmanned Aerial Vehicles (UAVs) has considerably grown in the civil sectors, due to their high flexibility of use. Currently, two important key points are making them more and more successful in the civil field, namely the decrease of production costs and the increase in navigation accuracy. In this paper, we propose a Kalman filtering-based sensor fusion algorithm, using a low cost navigation platform that contains an inertial measurement unit (IMU), five ultrasonic ranging sensors and an optical flow camera. The aim is to improve navigation in indoor or GPS-denied environments. A multi-rate version of the Extended Kalman Filter is considered to deal with the use of heterogeneous sensors with different sampling rates, and the presence of non-linearities in the model. The effectiveness of the proposed sensor platform is evaluated by means of numerical tests on the dynamic flight simulator of a quadrotor. Results show high precision and robustness of the attitude estimation algorithm, with a reduced computational cost, being ready to be implemented on low-cost platforms.

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.


Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2648 ◽  
Author(s):  
Antonio Delle Femine ◽  
Daniele Gallo ◽  
Carmine Landi ◽  
Mario Luiso

The widespread diffusion of Phasor Measurement Units (PMUs) is a becoming a need for the development of the “smartness” of power systems. However, PMU with accuracy compliant to the standard Institute of Electrical and Electronics Engineers (IEEE) C37.118.1-2011 and its amendment IEEE Std C37.118.1a-2014 have typically costs that constitute a brake for their diffusion. Therefore, in this paper, the design of a low-cost implementation of a PMU is presented. The low cost approach is followed in the design of all the building blocks of the PMU. A key feature of the presented approach is that the data acquisition, data processing and data communication are integrated in a single low cost microcontroller. The synchronization is obtained using a simple external Global Positioning System receiver, which does not provide a disciplined clock. The synchronization of sampling frequency, and thus of the measurement, to the Universal Time Coordinated, is obtained by means of a suitable signal processing technique. For this implementation, the Interpolated Discrete Fourier Transform has been used as the synchrophasor estimation algorithm. A thorough metrological characterization of the realized prototype in different test conditions proposed by the standards, using a high performance PMU calibrator, is also shown.


2021 ◽  
Vol 297 ◽  
pp. 01040
Author(s):  
Aziz El Fatimi ◽  
Adnane Addaim ◽  
Zouhair Guennoun

In a three-dimensional environment, the navigation of a vehicle in airspace, terrestrial space, or maritime space presents complex aspects concerning the determination of its position, its orientation, and the stability of the processing of the asynchronous data coming from the various sensors during navigation. In this context, this paper presents an experimental analysis of the position accuracy estimated by a low-cost inertial measurement unit coupled, by the extended Kalman data fusion algorithm, with a system of absolute measurements of a positioning system received from a GPS which designates the global positioning system. The different scenarios of the experimental study carried out during this work concerned three tests in a real environment, such as the navigation in a course inside the city of Rabat/Morocco with a moderate speed, a section on the highway at a speed of 120 Km/h and a circular path around a roundabout. The experimental results proved that the low-cost sensors studied are a good candidate for civil navigation applications.


2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Wang ◽  
Bo Song ◽  
Xueshuai Han ◽  
Yongping Hao

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.


2020 ◽  
pp. 101-111
Author(s):  
Gonzalo Perez-Paina ◽  
Claudio Paz ◽  
Martín Pucheta ◽  
Bruno Bianchini ◽  
Fernando Martínez ◽  
...  

The integration of down-looking camera with an in-ertial measurement unit (IMU) sensor makes possible to provide a lightweight and low-cost pose estimation system for unmanned aerial vehicles (UAVs) and micro-UAVs (MAVs). Recently, the authors developed an algorithm for IMU and exteroceptive sensor fusion filter for position and orientation estimation. The aim of the estimation is to be used in the outer control loop of an UAV for position control. This work presents an experimental set up to test that algorithm using an industrial robot to produce accurate planar trajectories as a safe alternative to testing the algorithm on real UAVs. The results of the IMU-camera fusion estimation for linear positions and linear velocities show an error admissible to be integrated on real UAVs.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8148
Author(s):  
Sana Sabah Al-azzawi ◽  
Siavash Khaksar ◽  
Emad Khdhair Hadi ◽  
Himanshu Agrawal ◽  
Iain Murray

Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect many aspects of children’s lives. The current visual assessment method for measuring head control ability and cervical range of motion (CROM) lacks accuracy and reliability. In this paper, a HeadUp system that is based on a low-cost, 9-axis, inertial measurement unit (IMU) is proposed to capture and evaluate the head control ability for children with CP. The proposed system wirelessly measures CROM in frontal, sagittal, and transverse planes during ordinary life activities. The system is designed to provide real-time, bidirectional communication with an Euler-based, sensor fusion algorithm (SFA) to estimate the head orientation and its control ability tracking. The experimental results for the proposed SFA show high accuracy in noise reduction with faster system response. The system is clinically tested on five typically developing children and five children with CP (age range: 2–5 years). The proposed HeadUp system can be implemented as a head control trainer in an entertaining way to motivate the child with CP to keep their head up.


Author(s):  
Amanpreet Kaur ◽  
Archana Mantri ◽  
Vipan Kumar

Background & Objective: MEMS sensors are rapidly growing as a sensing technology in all spheres of science and engineering. MEMS technology is playing an important role in avionics for miniaturization of systems and MEMS based Inertial Navigation System (INS) is one of the example. The situational awareness and performance of an aerial vehicle is computed with the help of an INS. This paper describes the case study for design of MEMS based low cost rugged INS for aerial vehicles. The 9 Degrees of Freedom (DOF) that are obtained from the sensors provide an inaccurate attitude information of aerial vehicles due to presence of external accelerations and the gyroscopic drifts in MEMS sensors. In order to overcome such problems and for the precise and reliable computation of orientation information, the error characteristics of accelerometers, magnetometers and gyroscopes have been combined into a sensor fusion algorithm with ‘Kalman Filter’ to compute the accurate orientation information. The processing has been done on STM32F407VGT6 microcontroller board. An accuracy of ± 0.1 degrees is achieved for Roll and Pitch and ± 1.0 degrees for Yaw have been obtained. The experimental results have been obtained in statically (keeping the device in a static position) and dynamically (rotating the device at different angles along roll, pitch and yaw axis) at room temperature of 22°C. Methods: The design is different in a way that it has used a unique combination of trio MEMS sensors network consisting of FXOS8700CQ Accelerometer, FXAS21000 Gyroscope, FXOS8700CQ Magnetometer. Results: The attitude estimation algorithm has been implemented on the 32-bit microcontroller. The information data is processed and displayed on 88.9 mm TFT-LCD through Graphical User Interface (GUI).


2014 ◽  
Vol 556-562 ◽  
pp. 1553-1559 ◽  
Author(s):  
Chang Liu Zha ◽  
Xi Lun Ding ◽  
Yu Shu Yu ◽  
Xue Qiang Wang

Micro Multi-propeller Multifunction Aerial Robot (MMAR) needs a low-cost, small-size, low-power and lightweight navigation system. To meet these requirements, a compact navigation system for micro MMARs is designed and implemented. The system consists of a GPS receiver, a MEMS inertial measurement unit (IMU), a barometer, a magnetometer and a dual microprocessors navigation processing platform. According to the characteristics of the sensors used, the optimized Kalman Filter (KF) is designed to implement the navigation data fusion with less computational cost. The test results show that the system’s small size, low power consumption, light weight and better dynamic accuracy can meet the requirements of micro MMAR autonomous flight.


Sign in / Sign up

Export Citation Format

Share Document