Angular velocity and torque estimation from vector measurements

Attitude and angular velocity estimation is essential for various applications of the user equipment (UE). In gyroscope-free scenarios, how to perform attitude and angular velocity estimation is a critical issue. In this letter, we propose a UE attitude and angular velocity estimation framework with the assistance of channel state information (CSI) variation. We first present a detailed proof of the existence of the mapping from CSI variation to angular velocity. Furthermore, we employ a neural network to learn this mapping, leveraging its capacity to model complex relationships. Then the estimated angular velocity is incorporated into the kinematic model of the gyroscope to derive the current attitude estimate of the UE. Simulation results further substantiate the significant potential of our method as a viable alternative to gyroscopes. Moreover, results also show that more propagation paths leads to better angular velocity and attitude estimation.

Based on an exact kinematics model, this paper considers two strategies aimed at diagnosing the health of a 3-axis rate gyro. In the first strategy, noisy attitude measurements are used to estimate angular velocity; comparing these estimates to the actual rate-gyro measurements provides the means for assessing the health of the rate gyro. In the second strategy, noisy attitude and angular velocity measurements are used to estimate the noise corrupting the rate-gyro measurements; analysis of the noise estimate provides an alternative means for assessing the health of the rate gyro. Both strategies are formulated as input and state estimation problems, where extended retrospective cost input estimation provides an estimate of the unknown input and the unscented Kalman filter provides the state estimate.

Angular Velocity(AV) estimation is necessary for controllers used for rotation motion monitoring. The velocity estimated is used for feedback and is very crucial in speed control for a variety of applications such as spacecraft tumbling detection, propeller speed control etc. Linear motion detection and linear velocity estimation has been exploited vastly. But, when it comes to angular velocity estimation of a randomly moving object with varying angular speeds, computation is very complex. Hence, computation of AV by non-contact based methods are in huge demand. The need for detection of the direction and value of rotation of a tumbling spacecraft in the Earth's orbit is a huge necessity. Accurate AV estimation has applications in spacecraft docking in space. In the proposed work we have developed an improved algorithm for real-time estimation of rotation using live video feed. The AV of the object can be measured by the camera feed of the rotating bodies as grabbed by the video camera. Angle is computed on every frame with respect to a reference and monitored for a certain number of frames to give the instantaneous AV. The number of frames processed by the camera is dynamic and is estimated prior to the angle calculation. Comparison of performance of AV estimation by the proposed algorithm with state-of-the-art Lucas Kanade(LK) object tracking method based AV estimation is also reported. The proposed approach shows significant improvement in estimating velocities whose motion is circular in nature. Additionally, this work advocates a novel approach towards an effective real-time angular velocity calculation by thresholding techniques, blob identification, and centroid detection.

Angular velocity is a critical parameter for satellites during on-orbit operation. Current gyro-free angular velocity estimation methods based on star sensors predominantly rely on multiple frames of star images and are effective under relatively small maneuvers of the star sensor. This paper proposes an angular velocity estimation approach utilizing single-frame star image data captured by a high-dynamic star sensor operating under rolling shutter exposure mode. By analyzing the motion patterns of star points in single-frame images, an angular velocity estimation optimization model was established with inter-star angular distances as the objective function. Accurate angular velocity estimation was achieved through the simulated annealing algorithm. To evaluate the star sensor's performance under high-dynamic conditions, three representative operational working conditions were simulated numerically. Experimental results demonstrate: under composite motion with dual-axis angular velocity of 15°/s and angular acceleration of 0.8°/s², the root mean square errors (RMSE) for triaxial angular velocity estimation are 0.0378°/s (X-axis), 0.0365°/s (Y-axis), and 0.1947°/s (Z-axis). Notably, when subjected to sinusoidal/cosinusoidal dynamic angular velocity excitation with amplitude of 15°/s, the maximum comprehensive error of triaxial angular velocity estimation does not exceed 0.103°/s, validating the algorithm's robustness in complex motion environments.

Kinematic information such as position, velocity, and acceleration is critical to determine the three-dimensional state of the robot in space. In this study, it is aimed to estimate as visual the linear and angular velocity of a mobile robot. Additionally, another aim of this study is to determine the suitability of the concatenation or subtraction layer in the Convolutional Neural Network (CNN) that will make this estimate. For these purposes, first, a simulation environment was created. 9000 pairs of images and necessary velocity information were collected from this simulation environment for training. Similarly, 1000 pairs of images and velocity information were gathered for validation. Four different CNN models were designed and these models were trained and tested using these datasets. As a result of the test, the lowest average error for linear velocity estimation was calculated as 0.93e-3m/s and angular velocity estimation was measured as 4.37e-3rad/s. It was observed that the results were sufficient for linear and angular velocity prediction according to statistical analysis of errors. In addition, it was observed that the subtraction layer can be used instead of the concatenation layer in the CNN architectures for hardware-limited systems. As a result, visual velocity estimation of mobile robots has been achieved with this study and the framework of CNN models has been drawn for this problem.

In this paper, we investigate the problem of setpoint stabilization and station keeping for Remotely Operated Underwater Vehicles. Manual control of such vehicles is a difficult task, due to environmental disturbances and complexity of the dynamics. The development of automatic control schemes to achieve station keeping and setpoint stabilization is complicated by the lack of precise information regarding the position, velocity and attitude of the vehicle with respect to an inertial reference. We propose a methodology for robust control design of a specific ROV model, which combines sensor information from sources of heterogeneous kind, namely a monocular CDD camera, a yaw compass, a depth sensor, and an inertial platform. The main feature of our approach is the incorporation in the controller structure of a suitable model of the disturbances, which reduces the tmcertainty in the vehicle attitude and position determination. The proposed approach is applied to the problem of robust station keeping of a specific model of a small commercial ROV, employed for pipeline inspection and maintenance of submerged offshore structure. Simulations performed on a 4 degrees-of-freedom model of the specific ROV show that the method is well suited to cope with imprecise sensor information and with environmental disturbances due to underwater current effects. Introduction A crucial problem in underwater automation is given by the scarcity of proper sensor information, especially regarding the real-time position of the vehicle with respect to a reference frame. A number of heterogeneous pieces of information (available at different sampling rates) must be made available to the control authority. As an example, typical information on the vehicle position come from CCD cameras, sonar and ultrasonic transponders, while information of the vehicle attitude are generally retrieved from compasses and rate gyros. In an underwater environment, the performance of these sensors, in terms of accuracy and bandwith, may be quite poor: for instance, signals available from sonar are available only at a very slow sampling rate. Signals from CCD cameras have a narrow bandwidth, and should not be differentiated to retrieve the vehicle velocity to avoid delays and high levels of noise in the control loop. On the other hand, signals from rate gyros and inertial platforms must be integrated to recover vehicle velocity and angular velocity, but this necessarily induces offsets and drifts in the measurements. The goal of this paper is to employ methodologies from nonlinear regulation theory to obtain sensor fusion and achieve setpoint control for a general model of small commercial ROVs. Our approach is to employ internal models of sensor drifts and disturbances to obtain sensor fusion directly at the controller level, rather than at the coordination level (that is, the guidance system) of the control authority. Moreover, internal model-based control is shown to improve the capability of the control system to reject environmental disturbances such as the underwater current. Besides the methodological contribution, our approach allows to endow the control and guidance scheme for ROVs with enhanced station-keeping capabilities.

In order to implement the sensorless electric drive for permanent magnet synchronous motor (PMSM), the rotor position observers were designed based on diagonal recurrent neural network (DRNN). The neural rotor position estimation process is separated into two neural observers, an stator current estimator, and angular velocity estimator. The former is used to map the estimated angular velocity and the applied terminal voltage to the estimated stator current. The neural velocity estimators used to map input voltage and current to the estimated PMSM angular velocity. The angle estimation block generates rotor angle by integrating the estimated angular velocity, with adjustment derived from the current estimation error. Through experimentation, 12 neurons in the hidden recurrent layer were found to produce good results for the neural current observer. The output layer contains two neurons: one for each the direct and quadrature axis current estimates. The simulation results show the estimated rotor angle error is small, and the robust property is good when step load changes.

We address the problem of attitude and rate gyro bias estimation based on inertial and range data. Range measurements are obtained from beacons installed in an inertial frame and acoustic sensors fixed in the moving body reference frame. A nonlinear observer is proposed and its stability and performance properties are studied using Lyapunov techniques. In the presence of constant bias on the rate gyro measurements, the attitude and bias estimates are shown to converge exponentially to the desired values inside an almost global region of attraction. For time-varying bias with bounded derivative, a local result of ultimate boundedness is provided. The performance of the proposed nonlinear observer is validated experimentally using a high-accuracy calibration table, which provides ground-truth signals for comparison with the resulting estimates.

A geometrical approach for the angular velocity determination using a star sensor

Robot navigation is one of the major fields of study in autonomous robotics (Oriolio, G., Ulivi, G. et al., 1998; Beetz, M., Arbuckle, T. et al., 2001; Wang, M. & Liu, J.N.K., 2004). In the present work, application of a multiresolutional filter for spatial information fusion in vision robot navigation is described. The novelty of the research is the enhanced estimation of the spatial sensory information in autonomous robotics by means of wavelet decomposition into multiresolutional levels and the fusion of the processed information while the processing takes place at the respective levels. Although wavelet-based information fusion is used in different applications (Hong, L., 1994; Hsin, H.C. & Li, A.C., 2006), its application in robotics is not common in literature. A wavelet-based filtering application in perceptual robotics is articulated earlier where human perception was central to the research. In the present work, optimal vision signal estimation for an autonomous robot is presented, where vision information is obtained by appropriate shaping of the visual sensory information and optimal estimation is obtained by a multiresolutional filter. One of the peculiarities of the research is essentially the wavelet-based dynamic filtering rather than static wavelet decomposition for image analysis or off-line decomposition for signal analysis. A multiresolutional dynamic filtering is a rather complex system. In this system vector form of wavelet decomposition is required as briefly mentioned by Hong (Hong, L., 1993) where the description of a multi-sensor fusion rather than wavelet decomposition was central to the study. In contrast to (Hong, L., 1993), in this work the multiresolutional dynamic filtering is central to the study together with the emphasis on application peculiarities in autonomous robotics; namely, several options for vector-wise operations are pointed out and one of the options is described in detail. Also, in autonomous robotics, the estimation of angular velocity is not a measurable quantity and it has to be estimated from the measurable state variables so that obstacle avoidance problem is taken care of. Therefore, the angular velocity estimation in real-time is a critical task in autonomous robotics and from this viewpoint the multiresolutional spatial information fusion process is desirable for enhanced robot performance. For the fusion process extended Kalman filtering can be used and is actually used in the present study. Perceptual vision employed in this study is one of the vision alternatives in robotics providing several essential possibilities characterizing the robot as to its performance. Especially perceptual robotics is gaining importance in the last decade as to its expected connection to human-like behaviour. The organization of the work is as follows. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude; thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle’s chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates.

A novel adaptive non-singular fast terminal sliding mode control (NFTSMC) scheme is developed for a highly nonlinear two-link robotic manipulator (TLRM) system subject to system uncertainties and unknown disturbances. First, to compensate for the system uncertainties, neural network (NN) is employed to approximate the unknown nonlinear dynamic function. Subsequently, to address the challenges of disturbance compensation and unmeasured joint angular velocity, a nonlinear NN-based extended state observer (NN-based ESO) is introduced. The NN-based ESO facilitates accurate disturbance estimation and compensation, while also enabling simultaneous estimation of the joint angular velocity. Through the integration of these techniques, the closed-loop system’s stability is proven using the direct Lyapunov method, demonstrating remarkable tracking performance within finite time. Simulation results validate the effectiveness of the NFTSMC with the NN-based ESO, showcasing superior performance compared to alternative control strategies.

In this paper, a robust adaptive sliding mode control strategy of micro electro-mechanical system (MEMS) triaxial gyroscope using radial basis function (RBF) neural network is presented for the system identification of MEMS gyroscope. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network controller is used to learn the unknown upper bound of model uncertainties and external disturbances. The adaptive RBF neural network is incorporated into the adaptive sliding mode control in the Lyapunov sense, and the stability of the proposed adaptive neural sliding mode control can be established. The dynamics and angular velocities of gyroscope can be identified in real time. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme, showing that the designed control system has better robust performance in its insensitivity to system nonlinearities; moreover, system parameters including angular velocity can be consistently estimated and tracking errors converge to zero asymptotically.

In conventional navigation systems, inertial sensors consist of accelerometers and gyroscopes. These sensors suffer from in‐built errors, accumulated drift and high‐level noise sensitivity. The accurate gyroscopes are expensive and not suitable in cost‐effective applications. To minimise such disadvantages, one solution is the combination of inertial sensors with different aiding sensors. To lower the cost, utilisation of redundant accelerometer structure as gyro‐free inertial measurement unit (GFIMU) has been proposed. In this study, Gyro‐Free navigation errors using four tri‐axial accelerometers are illustrated. Compensation of errors in terms of angular velocity and position estimation is verified based on adding a simple gyroscope, inexpensive stereo cameras as well as creating an easy to use topological map. The topological map is easily created by means of scale‐invariant feature transform method. The estimation of angular velocity is corrected on the basis of fusing the measurements from GFIMU and a simple gyroscope using unscented Kalman filter. The correction of position is performed by comparing the estimated position from GFIMU and observation of stereo cameras together with topological map. The results of the research show that the collaboration of GFIMU, stereo cameras and simple gyroscope will improve the robustness and accuracy of navigation, significantly.

H velocity aircraft and missiles are frequently guided by controlling the angular velocity of the control surfaces. To accomplish this guidance effectively, the primary element of the auto-pilot servo should detect one order higher than angular velocity, that is, angular acceleration. The detection of this angular acceleration is tradionally achieved by employing a rate gyro with a differentiating network in the output of its pick-off. Much simpler is to use an angular accelerometer directly to override the rate gyro, thus eliminating the necessity for a differentiating network; but past experience with conventional angular accelerometers has not been encouraging because of their undesirable sensitivity to linear acceleration and to angular velocity. Both of these handicaps have been overcome in a radically new invention which has been developed at Statham Laboratories. Fig. 1 shows the essentials of a Statham angular accelerometer. The novelty of this angular accelerometer is that its rotor consists of liquid only. When the case is subjected to an angular acceleration, the inertial forces will cause the liquid to rotate and develop a hydrostatic pressure against the paddle as the liquid is forced between the paddle and the baffles. The paddle will deflect angularly until the elastic restraint of the flexure and the pick-off balances the forces on the paddle. This novel construction has been adapted to both unbonded strain gage and inductive pick-offs. The advantages of the Statham liquid rotor angular accelerometer design are apparent after a careful perusal of Fig. 1. Since the liquid mass serves as the rotor, the weight of the paddle which must be supported along the sensitive axis can be made extremely low. In the construction of low range, rigid rotor angular accelerometers, because of the weight of the rotor, ball bearings are frequently employed to contain the rotor spindle. In the liquid rotor design, weak flexures are practical since only a small, rigid mass need be suspended.