Sijainnin estimointi inertiamittausyksikölla ilman paikannusjärjestelmää

This thesis aims to estimate the position of an inertial measurement unit (IMU) without any tracking device such as GPS. The work includes the calibration of the accelerometer with particle swarm optimization (PSO) to solve the equation, the gyrometer with the extended Kalman filter (EKF) and the magnetometer also with EKF. The calibration is realized with the data from the sensors and Matlab. When the calibration is done, the acceleration is obtained from the accelerometer and the gyrometer. The algorithm employs mostly rotation matrix theory. The performance of the algorithm depends on the success of the calibration. A small error in the estimation of the acceleration leads to a wrong result. This was, nevertheless, to be expected as a double integration with respect to time of a signal with remaining traces of bias is doomed to fail without any correction algorithms. Unfortunately, a working algorithm could not be achieved, pointing out that it may be difficult to realize one without external devices such as GPS.Tässä työssä estimoidaan inertiamittausyksikön (IMU) sijaintia käyttämättä GPS-laitetta. Työ sisältää kiihtyvyysanturin kalibroinnin hiukkasparvioptimointialgorithmilla (PSO), gyroskoopin laajennetulla Kalmanin suodattimella (EKF) ja kompassin EKF:lla. Kalibrointi on suoritettu vain anturien arvoilla ja Matlab-sovelluksella. Anturin kiihtyvyys saa kalibroiduilta kiihtyvyysanturilta ja kompassilta. Algorithmi käyttää rotaatiomatriisin teoria. Algorithmi tehokkuus riippuu kalibroinnista. Pienikin estimointivirhe aiheittaa väärän tuloksen.Työn tulokset voitiin ennustaa koska tuplaintegrointi pienellä virhellä johtaa helposti ja nopeasti tulokset väärään suuntaan. Työn algoritmi vaatii korjausalgoritmin joka pystyisi poistamaan integroinnin virheen. Valitettavasti toimivaa algoritmia ei löydettu, joka viittaa siihen, että sen toteutaminen saattaa olla vaikeaa ilman apulaitetta, kuten GPS-laitetta

Calibration and testing techniques for nanosatellite attitude system development in magnetic environment

Defence is held on 3.9.2021 12:00 – 15:00 https://aalto.zoom.us/j/62485002135The development of nanosatellites, which are generally defined by its weight ranging between 1kg-10kg, have grown considerably in the academia sector. The most popular form factor for spacecrafts in this category is the CubeSat form factor, as it opens up lower-cost launch opportunities and growing availability of commercial-off-the-shelf solutions for the spacecraft subsystems. For many nanosatellites orbiting in Low Earth Orbit, magnetic environment is an important aspect in its design consideration. This applies to both the platform engineering and mission design, as it influences the attitude system design as well as the design of the relevant scientific instruments. This thesis work contributes in the development of technology and techniques that can help in managing the influence of magnetic environment in spacecraft design, in particular the solution for magnetometers calibration and the detection of spacecraft residual magnetic dipole moment. The magnetometer calibration focuses on the implementation of rotational correction factor, which, in the more conventional techniques, is typically assumed to be in a certain condition. The detection of spacecraft residual magnetic dipole moment focuses on the early development of a machine-vision-assisted test bed that aims to reduce the mechanical and electrical complexity of the more common spacecraft automated magnetic test bed. Several missions, where the spacecraft has been developed and built in Aalto university, has been launched. From the perspective of practical performance of in-orbit spacecraft operation, this thesis work will also discuss the challenges and lessons learned from the operation of Aalto-1 attitude system. This thesis contribution is focused on the attitude analysis and sensors calibration of the Aalto-1 in-orbit operation

CubeSat Attitude System Calibration and Testing

This thesis concentrates on the development of Aalto-2 CubeSat attitude system calibration and testing methods. The work covers the design and testing phase of the calibration algorithms to the analysis of experimental data in order to verify the performance of the attitude instruments. The instruments under test are two-axis digital Sun sensor, three-axis magnetometer, three-axis gyroscope, and three-axis magnetorquer. These devices are all commercial off-the-shelf components which are selected for their cost-to-performance efficiency. The Sun sensor and gyroscope were calibrated with linear batch least squares method and the results showed that only minor corrections were required for the Sun angle and angular velocity readings, while the brightness readings from the Sun sensor required more corrections. For magnetometer calibration, a specific particle swarm optimization algorithm was developed with novel approach to estimate the full calibration parameters, without having to simplify the sensor model. The calibration results were evaluated with simulation data with satisfying results, while the results from experimental data itself showed heading error improvement from \SIrange[range-phrase=--]{5.24}{13.24}{\degree} to \SIrange[range-phrase=--]{1.9}{7.3}{\degree} for unfiltered data. Besides the magnetometer calibration parameters estimation, the magnetic properties of the spacecraft were also analyzed using inverse multiple magnetic dipole modeling approach, where multiple magnetic dipoles positions and moments are estimated using particle swarm optimization from the magnetic field strength readings around the spacecraft. The estimated total residual magnetic moment of the spacecraft is \SI{58.5}{\milli\ampere\square\meter}, lower than the maximum magnetorquer moment which is \SI{0.2}{\ampere\square\meter} in each axis. The magnetorquer was tested for verifying the validity of magnetic moment generated by the magnetorquer. The result shows that the magnetorquer moment is nonlinear, in contrast to the linear theoretical model

Two-step calibration methods for miniature inertial and magnetic sensor units

Low-cost inertial/magnetic sensor units have been extensively used to determine sensor attitude information for a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation systems, to biomotion analysis and biomedical applications. In order to achieve precise attitude reconstruction, appropriate sensor calibration procedures must be performed in advance to process sensor readings properly. In this paper, we are aiming to calibrate different error parameters, such as sensor sensitivity/scale factor error, offset/bias error, nonorthogonality error, mounting error, and also soft iron and hard iron errors for magnetometers. Instead of estimating all of these parameters individually, these errors are combined together as the combined bias and transformation matrix. Two-step approaches are proposed to determine the combined bias and transformation matrix separately. For the accelerometer and magnetometer, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings, and the transformation matrix is then derived through a two-step iterative algorithm by exploring the intrinsic relationship among sensor readings. For the gyroscope, the combined bias can be easily determined by placing the sensor node stationary. For the transformation matrix estimation, the intrinsic relationship among gyroscope readings is explored again, and an unscented Kalman filter is employed to determine such matrix. The calibration methods are then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration methods

Micromagnetometer calibration for accurate orientation estimation

Micromagnetometers, together with inertial sensors, are widely used for attitude estimation for a wide variety of applications. However, appropriate sensor calibration, which is essential to the accuracy of attitude reconstruction, must be performed in advance. Thus far, many different magnetometer calibration methods have been proposed to compensate for errors such as scale, offset, and nonorthogonality. They have also been used for obviate magnetic errors due to soft and hard iron. However, in order to combine the magnetometer with inertial sensor for attitude reconstruction, alignment difference between the magnetometer and the axes of the inertial sensor must be determined as well. This paper proposes a practical means of sensor error correction by simultaneous consideration of sensor errors, magnetic errors, and alignment difference. We take the summation of the offset and hard iron error as the combined bias and then amalgamate the alignment difference and all the other errors as a transformation matrix. A two-step approach is presented to determine the combined bias and transformation matrix separately. In the first step, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings. In the second step, the intrinsic relationships of the raw sensor readings are explored to estimate the transformation matrix as a homogeneous linear least-squares problem. Singular value decomposition is then applied to estimate both the transformation matrix and magnetic vector. The proposed method is then applied to calibrate our sensor node. Although there is no ground truth for the combined bias and transformation matrix for our node, the consistency of calibration results among different trials and less than 3° root mean square error for orientation estimation have been achieved, which illustrates the effectiveness of the proposed sensor calibration method for practical applications

Deterministic and stochastic error modeling of inertial sensors and magnetometers

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.This thesis focuses on the deterministic and stochastic modeling and model parameter estimation of two commonly employed inertial measurement units. Each unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. In the first part of the thesis, deterministic modeling and calibration of the units are performed, based on real test data acquired from a flight motion simulator. The deterministic modeling and identification of accelerometers is performed based on a traditional model. A novel technique is proposed for the deterministic modeling of the gyroscopes, relaxing the test bed requirement and enabling their in-use calibration. This is followed by the presentation of a new sensor measurement model for magnetometers that improves the calibration error by modeling the orientation-dependent magnetic disturbances in a gimbaled angular position control machine. Model-based Levenberg-Marquardt and modelfree evolutionary optimization algorithms are adopted to estimate the calibration parameters of sensors. In the second part of the thesis, stochastic error modeling of the two inertial sensor units is addressed. Maximum likelihood estimation is employed for estimating the parameters of the different noise components of the sensors, after the dominant noise components are identified. Evolutionary and gradient-based optimization algorithms are implemented to maximize the likelihood function, namely particle swarm optimization and gradient-ascent optimization. The performance of the proposed algorithm is verified through experiments and the results are compared to the classical Allan variance technique. The results obtained with the proposed approach have higher accuracy and require a smaller sample data size, resulting in calibration experiments of shorter duration. Finally, the two sensor units are compared in terms of repeatability, present measurement noise, and unaided navigation performance.Seçer, GörkemM.S

Magnetic attitude control of LAICE satellite with aerodynamic stabilization

The Lower Atmosphere Ionosphere Coupling Experiment (LAICE) is a NanoSatellite which will be performing in-situ measurements of neutral and ion densities in the mesosphere, lower thermosphere, and ionosphere and correlating them to measurements of gravity waves in the lower atmosphere. The satellite is based on a new 6U CubeSat form factor based on the IlliniSat 2 bus developed at the University of Illinois at Urbana-Champaign. The satellite’s payloads need to be oriented such that three instruments are oriented along the velocity direction of the satellite, while a fourth instrument is pointed towards nadir. The attitude determination and control system must achieve the attitude pointing requirements (5° from nominal attitude) with minimum of cost and low power. The satellite will therefore make use of magnetic torque coils augmented with aerodynamic stabilization to accomplish the mission attitude control requirements. The proposed control method relies on passive aerodynamic stabilization of the spacecraft to maintain pointing in the satellite normal frame. The aerodynamic stabilization reduces the dimensionality of the magnetic attitude control to a one-dimensional problem. The major contributions of this work include the development of an object-oriented attitude control library with which to program flight code as well as simulate satellite dynamics to validate the flight code; the development and tuning of an Extended Kalman Filter for attitude determination using low-cost magnetometers and MEMS gyros; the development and tuning of a hybrid attitude control algorithm for magnetic torque coils which can reliably detumble and reorient the satellite; the development of an efficient graphical drag model for computing aerodynamic torques; and the novel use of a tri-axial Helmholtz cage for performing a Hardware-in-Loop optimization of the coupled attitude determination and control system

Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

Online Inertial Measurement Unit Sensor Bias And Attitude Estimation For The Calibration And Improved Performance Of Attitude And Heading Reference Systems

Dynamic instrumentation and estimation of vehicle attitude is critical to the accurate navigation of land, sea, and air vehicles in dynamic motion. The focus of this thesis is the development of algorithms for improved performance of attitude and heading reference systems (AHRSs) and robotic vehicle navigation. inertial measurement unit (IMU) sensor bias estimation methods for use in the calibration of AHRSs and an adaptive attitude estimator operating directly of SO(3) are reported. The reported algorithms provide online calibration and attitude estimation methods which enable more accurate navigation for robotic vehicles. This thesis differentiates AHRSs into two categories – AHRSs that estimate true-North heading and those that estimate magnetic north heading. Chapters 3-5 report several novel algorithms for micro-electro-mechanical systems (MEMS) IMU sensor bias estimation. Observability, stability, and parameter convergence are evaluated in numerical simulations, full-scale vehicle laboratory experiments, and full-scale field trials in the Chesapeake Bay, MD. Chapter 6 reports an adaptive sensor bias observer and attitude observer operating directly on SO(3) for true-North gyrocompass systems that utilize six-degrees of freedom (DOF) IMUs with three-axis accelerometers and three-axis angular rate gyroscopes (without magnetometers) to dynamically estimate the instrument’s time-varying true-North attitude (roll, pitch, and geodetic heading) in real-time while the instrument is subject to a priori unknown rotations. Stability proofs for the reported bias and attitude observers, preliminary simulations, and a full-scale vehicle trial are reported. The presented calibration methods are shown experimentally to improve calibration of AHRS attitude estimation over current state of the art sensor bias estimation methods, and this thesis presents a true-North gyrocompass system based on adaptive observers for use with strap-down IMUs. These results may prove to be useful in the development of navigation systems for small low-cost robotic vehicles