An Improved Real-Time Approach for Video based Angular Motion Detection and Measurement

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.

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.

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.

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.

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.

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

A novel rotor velocity estimation scheme applicable to vector controlled induction motors has been described. The proposed method evaluates rotor velocity, /spl omega//sub r/, online, does not require any extra transducers or injection of any signals, nor does it employ complicated algorithms such as MRACS or Kalman filters. Furthermore, the new scheme operates at all velocities including zero with very little error. The procedure employs motor model equations; however all differential and integral terms have been eliminated giving a very fast, low-cost, and practical alternative to methods currently available. Furthermore, the resistance estimation technique is also implemented. Not only does this ensure the tuned operation of the vector controller, but also results in the aforementioned velocity estimator being completely parameter insensitive. A detailed and thorough explanation of the underlying theory of both velocity and parameter estimation techniques is given. These are supported by various simulation tests, taking into consideration the PWM inverter, and practical results.

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.

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.

The mechanism for motion detection in a fly's vision system, known as the Reichardt correlator, suffers from a main shortcoming as a velocity estimator: low accuracy. To enable accurate velocity estimation, responses of the Reichardt correlator to image sequences are analyzed in this paper. An elaborated model with additional preprocessing modules is proposed. The relative error of velocity estimation is significantly reduced by establishing a real-time response-velocity lookup table based on the power spectrum analysis of the input signal. By exploiting the improved velocity estimation accuracy and the simple structure of the Reichardt correlator, a high-speed vision system of 1 kHz is designed and applied for robot yaw-angle control in real-time experiments. The experimental results demonstrate the potential and feasibility of applying insect-inspired motion detection to robot control.

Background: Space docking technology and space docking mechanism are indispensable parts of modern complex spacecraft and important means to expand satellite functions in the future. However, there is still some technology to be broken through in the research of their buffer and locking, so that the docking mechanism can be better used in the future. Objectives: Through the introduction and discussion of the characteristics of space docking mechanism patents in recent years, some valuable conclusions are summarized, and the future research and development of space docking mechanism are prospected. Methods: This paper studied the patents of various docking mechanism, and summarized the patents and research progress of docking mechanism in space. Results: With the development of space industry, docking mechanism has become more and more important. So the docking mechanism are needed to achieve the replenishment for long-term spacecraft in orbit, personnel exchange, spacecraft maintenance, and so on. Conclusion: By elaborating on the structural characteristics of the docking mechanism, the importance of the docking mechanism is consulted for investigation. Through the comparison of these patents, it is concluded that weak impact docking mechanism and electromagnetic docking mechanism are the main development trends of docking institutions in the future.

In this work, a sensorless control scheme was designed for permanent magnet synchronous motors based on a sampled model. The discrete-time model was obtained using the Symplectic Euler method. Taking such model along with voltage and current measurements, an observer is designed for rotor position and velocity estimation. Then, the rotor velocity is forced to track a desired reference signal by means of a discrete-time quasi-sliding mode technique.

Angular velocity and torque estimation from vector measurements

Open-chain manipulator robots play an important role in the industry, since they are utilized in applications requiring precise motion. High-performance motion of a robot system mainly relies on adequate trajectory planning and the controller that coordinates the movement. The controller performance depends of both, the employed control law and the sensor feedback. Optical encoder feedback is the most used sensor for angular position estimation of each joint in the robot, since they feature accurate and low noise angular position measurements. However, it cannot detect mechanical imperfections and deformations common in open chain robots. Moreover, velocity and acceleration cannot be extracted from the encoder data without adding phase delays. Sensor fusion techniques are found to be a good solution for solving this problem. However, few works has been carried out in serial robots for kinematic estimation of angular position, velocity and acceleration, since the delays induced by the filtering techniques avoids its use as controller feedback. This work proposes a novel sensor-fusion-based feedback system capable of providing complete kinematic information from each joint in 4-degrees of freedom serial robot, with the contribution of a proposed methodology based on Kalman filtering for fusing the information from optical encoder, gyroscope and accelerometer appended to the robot. Calibration and experimentation are carried out for validating the proposal. The results are compared with another kinematic estimation technique finding that this proposal provides more information about the robot movement without adding state delays, which is important for being used as controller feedback.