Initial Alignment Error Research Articles

EA-CTFVS: An Environment-Agnostic Coarse-to-Fine Visual Servoing Method for Sub-Millimeter-Accurate Assembly

Peg-in-hole assembly, a crucial component of robotic automation in manufacturing, continues to pose challenges due to its strict tolerance requirements. To date, most conventional peg-in-hole assembly algorithms have been validated only within simulated environments or under limited observational scenarios. In this paper, an environment-agnostic coarse-to-fine visual servoing (EA-CTFVS) assembly algorithm is proposed. Firstly, to solve the frequent issue of visual blindness during visual servoing, a bottleneck pose is proposed to be used as the desired pose for the visual servoing. Secondly, to achieve accurate assembly, a coarse-to-fine framework is constructed, in which a rough pose is given by the coarse controller to remove large initial alignment errors. For the fine controller, a twin network-based fine controller is provided to improve assembly accuracy. Furthermore, EA-CTFVS utilizes the Oriented Bounding Box (OBB) of objects as the input for visual servoing, which guarantees the system’s ability to operate effectively in diverse and complex scenes. The proposed EA-CTFVS achieves a successful assembly rate of 0.92/0.89 for initial alignment errors of 15/30 cm and 0.6 mm tolerance in real-world D-sub plug assembly tasks under complex scenarios.

Design of a Multi-Position Alignment Scheme.

The current new type of inertial navigation system, including rotating inertial navigation systems and three-autonomy inertial navigation systems, has been increasingly widely applied. Benefited by the rotating mechanisms of these inertial navigation systems, alignment accuracy can be significantly enhanced by implementing IMU (Inertial Measurement Unit) rotation during the alignment process. The principle of suppressing initial alignment errors using rotational modulation technology was investigated, and the impact of various component error terms on alignment accuracy of IMU during rotation was analyzed. A corresponding error suppression scheme was designed to overcome the shortcoming of the significant scale factor error of fiber optic gyroscopes, and the research content of this paper is validated through corresponding simulations and experiments. The results indicate that the designed alignment scheme can effectively suppress the gyro scale factor error introduced by angular motion and improve alignment accuracy.

A self-adaptive high precision gripper for shape variant components: Towards higher reliability and efficiency of a cobotic cell

With continuously shrinking batch sizes and increasing order numbers the handling of different object variants becomes a key parameter for the automatization of assembly processes in Industry 4.0. Many compliant and underactuated grasping devices that exploit hierarchical principles, including whipple trees, tendon-pulley systems, pin arrays, fluid differentials, and air pressure, just to name a few, are subject to research. However, these often bionically inspired systems are mostly used for humanoid applications, such as prosthesis, dexterous hands, and crop harvesting. While they can achieve a significant grasp robustness, their precision is often too low for industrial manufacturing processes. Therefore, this paper proposes a deterministic grasping system with one level of adaptation to grasp a great number of object variants in a cobotic assembly cell. This grasping system not only adapts to different shapes but also compensates initial position and alignment errors and guarantees a predefined centring within the grasp. The technology has been tested intensively in the laboratory and subsequently demonstrated in the cobotic cell, where it decreased the machine breakdown time due to pick and place failures by 90%.

All-Parameter System-Level Calibration for SINS/CNS Based on Global Observability Analysis

Before using the strap-down inertial navigation system (SINS)/ celestial navigation system (CNS) integrated navigation system, the calibration of its error parameters is a necessary process to improve the system accuracy. In this paper, an all-parameter system-level calibration method has been proposed, which utilizes Kalman filtering for simultaneously estimating the bias, scale factor, misalignments of inertial measurement unit (IMU), and installation errors of the star sensor. The observation has been constructed using the inertial navigation output information and the starlight vector measured by the star sensor. The observability of the error parameters has been analyzed theoretically, and the optimal excitation conditions for each error parameter are obtained. The effects of the angle between the starlight vector and the optical axis of the star sensor, and the attitude of the IMU, on the estimation accuracy of the installation error of the star sensor are also given. Finally, a 10-order rotation scheme has been designed and all the error parameters can be optimally excited. The simulation and experimental results demonstrate that all the error parameters have better stability and repeatability, when compared with the traditional method, and the initial alignment error of 3.4" is better than 13.1" of the traditional method. Moreover, the installation error of the star sensor can be estimated with a high precision only when the angle between the starlight vector and the optical axis of the star sensor exceeds 1°. It is shown that the proposed method can effectively improve the navigation performance of the SINS/CNS integrated navigation system.

An optimal calibration method for gyro and star sensor based on observability analysis

The major challenge of current strapdown inertial navigation system/celestial navigation system (SINS/RCNS) is failing to calibrate all the error coefficients of gyro and star sensor in-flight accurately, which will lead to the attitude accuracy degradation. To address this question, this paper proposes an optimal calibration method based on observability analysis. Here, we derive the star sensor measurement model with respect to initial alignment errors, gyro errors, and star sensor installation errors. And the observability analysis is employed to explain the reason why all the error coefficients can be estimated effectively by three maneuvers merely. Finally, the optimal observation sequence is determined based on the fisher information matrix (FIM). The proposed optimal calibration method is evaluated by a representative suborbital flight vehicle trajectory, which represents significant improvements in estimation accuracy of error coefficients, and realizes the attitude accuracy enhancements.

Study on the Shearer Attitude Sensing Error Compensation Method Based on Strapdown Inertial Navigation System

Intelligent mining is the demand and inevitable development direction of China’s coal industry, and the shearer attitude based on the 3D spatial scale is the essential basic information for realizing intelligent mining at the fully mechanized mining face. The traditional shearer attitude monitoring technology cannot meet the accuracy requirements of attitude sensing of mining equipment at an intelligent working face. In this paper, based on the basic principle of strapdown inertial navigation system (SINS), a real-time solution algorithm for shearer attitude is constructed, and the specific process of SINS coarse alignment and fine alignment is studied. The main error terms affecting the accuracy of SINS are analyzed, and the initial alignment error, installation deviation angle error, and vibration effect error are compensated and analyzed through simulation one by one. According to the working characteristics of the shearer, the coning error and sculling error of the shearer’s SINS are compensated and analyzed through simulation. This paper provides a complete set of theories and methods for improvement of the accuracy of the shearer attitude sensing in underground operation; simulation results also verify that the theories and methods can significantly improve the perception accuracy which can provide theoretical and technical reference for the production state prediction of fully mechanized mining face and intelligent control of underground mining equipment.

Robust adaptive learning for attitude control of rigid bodies with initial alignment errors

In the attitude tracking control tasks of rigid-body systems, initial alignment errors are recognized as one of the main factors that hinder the effective controller design and thus the control accuracy improvement. To overcome this difficulty, we develop a novel adaptive iterative learning control (ILC) scheme to achieve the attitude tracking control tasks with high precision for rigid bodies. Owing to the newly employed deadzone mechanism in the parametric updating law, the proposed adaptive ILC scheme is able to deal with initial alignment errors properly without imposing extra requirements to the system dynamics. Moreover, by utilizing the quaternion-based description in the system dynamics, various uncertainties, including external iteration-varying disturbances and unknown system parameters, can be addressed together by a single scalar estimation scheme. The convergence of the proposed adaptive ILC scheme is analyzed rigorously with a Lyapunov-like theory by virtue of newly introducing a modified composite energy function. In addition, to demonstrate its effectiveness, the proposed adaptive ILC method is applied to the attitude tracking of a spacecraft, which shows excellent control performances despite various uncertainties.

Improvement of a computer-aided alignment algorithm for the nonsymmetric off-axis reflective telescope

Due to complications of the off-axis three-mirror anastigmat (TMA) telescope, each optical element in the off-axis TMA telescope is introduced with theoretical eccentricity and tilt. Moreover, the introduction of freeform surfaces and other optical elements with complex surface features generally causes the initial alignment accuracy of the optical path to be low. A large initial alignment error amplifies the sensitivity of the misalignment calculation accuracy to the measurement error of the Zernike coefficient, resulting in difficulty obtaining convergence results for a computer-aided alignment algorithm. Considering the above issues, the alignment sensitivity of each component in the optical path is analyzed in this. The large conditional number of the sensitivity matrix results in poor algorithm robustness. Thus, an adaptive damping factor least-squares algorithm model is proposed and derived to improve the efficiency of the classical least-squares algorithm. A method for piecewise optimization of the damping factor is also deduced. Experiments based on a 0.6 m off-axis TMA telescope verify the effectiveness of the algorithm. Simulation and integration experiments show that the proposed method can reduce the accuracy requirements of the initial alignment and improve the adaptability of Zernike coefficient measurement noise. The alignment procedure is carried out for three iterations, and the average of the five field-of-view wave aberration values is enhanced from 2.1λ (RMS; λ=632.8nm) to 0.09λ (average). The improved algorithm can solve the large initial alignment error of a nonsymmetrical off-axis reflective optical system with a freeform surface as well as the problem of the low success rate of the misalignment value due to low Zernike coefficient measurement accuracy.

SINS/GNSS-Integrated Navigation of Surface Vessels Based on Various Nonlinear Kalman Filters and Large Ship Dynamics

Generally, studies investigating the strapdown inertial navigation system/global navigation satellite system (SINS/GNSS)-integrated navigation of surface vessels focus on adaptive strong nonlinear algorithms to handle the system and measurement noise induced by the complex environment and motion state. However, these studies rarely consider the suitability of strong nonlinear adaptive optimization in all conditions or the existence of any restriction. The application conditions for these standard nonlinear filters with respect to the surface vessel SINS/GNSS require investigation. In this study, the estimation accuracy of the motion state obtained using the extended Kalman filter, unscented Kalman filter, and cubature Kalman filter in case of different vessel motions is compared based on the simulated ship sensor data obtained using a dynamic large surface vessel model under various marine conditions. Compared with previous studies, the data generator in this study simulates the actual ship movements under various conditions, based on which considerably detailed and practical analysis and conclusion can be realized in case of the SINS/GNSS-integrated navigation obtained using various standard nonlinear filters. In particular, the situations often encountered by large surface vessels during integrated navigation attributed to environmental interference or instrument failure, including system noise amplification, initial alignment error, and GPS outage, are investigated.

Summarization of Vehicle Position and Azimuth Fast Determining Technology

To improve the rapidity, accuracy and autonomy of vehicle position and azimuth determining is an important theme in the development of land launch equipment. Based on this topic, a comprehensive summary is made of the theories and methods related to vehicle position and azimuth determining technology, including autonomous integrated navigation, initial alignment technology, error estimation and compensation technology, observability analysis method, and nonlinear filtering method. By summarizing and analyzing the related progress in recent years, this paper points out the existing problems in the research, and probes into the development direction of vehicle position and azimuth fast determining technology.

Study on the observability degree of integrated inertial navigation system of autonomous underwater vehicle

Strapdown inertial navigation system (SINS) initial alignment error model is presented under moving base. SINS navigation error models and the integrated navigation error models are proposed. The state observability during the initial alignment under moving base was thoroughly studied according to the piece-wise constant system method and singular value decomposition method. Simulation experiments were carried out under different vehicle movements with same integration and different integration with same vehicle movements respectively. Simulation experiments show that the observability degree of states variables can be improved under linear or rotation movement, furthermore, the outer measurements aided SINS can also improve the observability degree of system state variables accordingly. The observability degree increases of state variables improve the accuracy of estimation and the speed of convergence of Kalman filter applied in the integrated inertial navigation system.

Study on the observability degree of integrated inertial navigation system of autonomous underwater vehicle

Strapdown inertial navigation system (SINS) initial alignment error model is presented under moving base. SINS navigation error models and the integrated navigation error models are proposed. The state observability during the initial alignment under moving base was thoroughly studied according to the piece-wise constant system method and singular value decomposition method. Simulation experiments were carried out under different vehicle movements with same integration and different integration with same vehicle movements respectively. Simulation experiments show that the observability degree of states variables can be improved under linear or rotation movement, furthermore, the outer measurements aided SINS can also improve the observability degree of system state variables accordingly. The observability degree increases of state variables improve the accuracy of estimation and the speed of convergence of Kalman filter applied in the integrated inertial navigation system.

Hybrid IRBM-BPNN Approach for Error Parameter Estimation of SINS on Aircraft.

To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method.

A Self-Alignment Method of MEMS Biaxial Rotation Modulation Strapdown Compass for Marine Applications

Self-alignment of strapdown inertial navigation systems incorporating micro-electro-mechanical systems (MSINS) is a great challenge for marine applications. In this paper, a self-alignment method for a rotating MEMS strapdown compass is proposed with the aim of solving this problem. First, based on an analysis of biaxial rotation modulation and initial alignment of the strapdown compass, a self-alignment method is presented and verified. Second, by analyzing the effects of biaxial rotation modulation, the proposed method is improved by speeding up the rotation and reducing the stop time of the biaxial rotation mechanism to shorten the initial alignment time, which effectively suppresses the influence of MEMS noise on the initial alignment error angle. The influence of ship swinging on the initial alignment error angle is also analyzed. The efficiency of the method is verified by experiments on a swinging base. Finally, a parameter adjustment approach is presented that allows the proposed method to be used with different types of MEMS. This approach is validated by experiments. All the experimental results demonstrate the efficiency and precision of the proposed method.

Initial Alignment Error On-Line Identification Based on Adaptive Particle Swarm Optimization Algorithm

To solve the problem of high accuracy initial alignment of strap-down inertial navigation system (SINS) for ballistic missile, an on-line identification method of initial alignment error based on adaptive particle swarm optimization (PSO) is proposed. Firstly, a complete navigation model of SINS is established to provide the accurate model basis for subsequent numerical optimization calculation. Then setting the initial alignment error as the optimization parameter and regarding the minimum deviation between SINS and GPS output as the objective function, the error parameter optimization model is designed. At the same time, the mutation idea of genetic algorithm (GA) is introduced into the PSO; thus the adaptive PSO is adopted to identify the initial alignment error on-line. The simulation results show that it is feasible to solve the initial alignment error identification problem of SINS by intelligent optimization algorithm. Compared with the standard PSO algorithm and the GA, the adaptive PSO algorithm has the fastest convergence speed and the highest convergence precision, and the initial pitch error and the initial yaw error precision are within 10′′ and the initial azimuth error precision is within 25′′. The navigation accuracy of SINS is improved effectively. Finally, the feasibility of the adaptive PSO algorithm to identify the initial alignment error is further validated based on the test data.

Anti-Disturbance Initial Alignment Method Based on Quadratic Integral for Airborne Distributed POS

Airborne distributed position and orientation system (POS) is playing an important role in the high-resolution aerial survey and a remote sensing system. It is composed of a master inertial navigation system (INS), the global navigation satellite system, and several slave INSs. However, the high-frequency vibration disturbance of aircraft engine and the flexure deformation disturbance of lever arm could lead to serious initial alignment errors of slave INSs. In order to solve the problem, an anti-disturbance initial alignment method based on quadratic integral for airborne distributed POS is proposed. A coarse alignment based on quadratic integral in inertial frame is proposed to reduce the disturbances of high-frequency vibration and flexure deformation. Furthermore, the disturbance of flexure deformation is considered as the first-order Markov process, and is added into the fine transfer alignment model. The experimental results verified that the proposed method can effectively improve the initial alignment of the distributed POS, especially the azimuth precision of two slave INSs which are improved by 0.0260° and 0.0167°, respectively.

Effect of the gyroplatform control error on the accuracy of the initial azimuth alignment of the gyro inclinometer

The effect of the gyroplatform control error on the accuracy of the initial azimuth alignment of the inclinometer is analyzed. A mathematical model of the gyroinertial module of the inclinometer has been constructed and studied. A method to compensate for the initial azimuth alignment error caused by the gyroplatform control error is proposed.

Research on Adaptive Kalman Filter for INS

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

Terrestrial self-contained navigation system based on a novel laser Doppler velocimeter

In order to improve the accuracy and manoeuvrability of terrestrial self-contained navigation system, an idea of building an integrated navigation system using a novel laser Doppler velocimeter together with an inertial navigation system is proposed. The structure and performance of the split reuse-type structure of the laser Doppler velocimeter are expounded, and the integrated technology of marching initial alignment and real-time errors compensation are also discussed in detail. The static inertial alignment of the inertial navigation system can be skipped over and the position error of navigation has been decreased from 1180 m in 2 h to 6 m in 2 h. The experiment results show that the strapdown inertial navigation system/laser Doppler velocimeter integrated navigation system can work effectively without parking and the high-accuracy self-contained navigation can be achieved.

In-Flight Alignment of POS Based on State-Transition Matrix

Position and orientation system (POS) is an important part of airborne remote sensing system, it is used to measure and compensate the motion errors of airborne sensors in course of imaging. In-flight alignment is an effective way to improve the accuracy and robustness of POS in its operating cycles. In the traditional in-flight alignment methods, large initial alignment errors result in the fact that nonlinear error models and nonlinear filters must be used, which means much calculation load will accompany the system. To avoid the large initial alignment errors, in this paper, a new in-flight alignment method with fewer calculation loads is proposed. The error propagate characteristic of POS is analyzed first, and state transition matrix is used to record the initial alignment errors propagate process in every calculation step. After observability analysis of the new method, the in-flight alignment is carried out according to the navigation errors and the error state-transition matrix of POS. To validate the proposed in-flight alignment method, car-mounted experiment and flight test are carried out. Experiment results show that, the proposed in-flight alignment method can improve the accuracy and the robustness of POS with fewer calculation loads.