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This paper aims to present a novel transfer alignment method based on combined double-time observations with velocity and attitude for ships’ poor maneuverability to address the system errors introduced by flexural deformation and installing which are difficult to calibrate.

Based on velocity and attitude matching, redesigning and deducing Kalman filter model by combining double-time observation. By introducing the sampling of the previous update cycle of the strapdown inertial navigation system (SINS), current observation subtracts previous observation are used as measurements for transfer alignment filter, system error in measurement introduced by deformation and installing can be effectively removed.

The results of simulations and turntable tests show that when there is a system error, the proposed method can improve alignment accuracy, shorten the alignment process and not require any active maneuvers or additional sensor equipment.

Calibrating those deformations and installing errors during transfer alignment need special maneuvers along different axes, which is difficult to fulfill for ships’ poor maneuverability. Without additional sensor equipment and active maneuvers, the system errors in attitude measurement can be eliminated by the proposed algorithms, meanwhile improving the accuracy of the shipboard SINS transfer alignment.

Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy.

First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency.

The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process.

This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation.

In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation.

Point set calibration is a building block in the field of intelligent manufacturing.

The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation.

During the process of the robotic grinding and polishing operations on aero-engine blades, the key problem of calibration error lies in fixture error and uneven margin. To solve this problem, this paper aims to propose a novel method to achieve rapid online calibration of the workpiece coordinate system through laser-based measurement techniques.

The authors propose a calibration strategy based on point cloud registration algorithm. The main principle is presented as follows: aero blade mounted on clamping end-effector is hold by industry robot, the whole device is then scanned by a 3D laser scanner to obtain its surface point cloud, and a fast segmentation method is used to acquire the point cloud of the workpiece. Combining Super4PCS algorithm with trimmed iterative closest point, we can align the key points of the scanned point cloud and the sampled points of the blade model, thus obtaining the translation and rotation matrix for calculating the workpiece coordinate and machining allowance. The proposed calibration strategy is experimentally validated, and the positioning error, as well as the margin distribution, is finally analyzed.

The experimental results show that the algorithm can well accomplish the task of cross-source, partial data and similar local features of blade point cloud registration with high precision. The total time spent on point cloud alignment of 100,000 order of magnitude blade is about 4.2 s, and meanwhile, the average point cloud alignment error is reduced to below 0.05 mm.

An improved point cloud registration method is proposed and introduced into the calibration process of a robotic system. The online calibration technique improves the accuracy and efficiency of the calibration process and enhances the automation of the robotic grinding and polishing system.

The study aims to propose reverse processing solution to improve the performance of strapdown inertial navigation system (SINS) initial alignment and SINS-/global positioning system- (GPS) integrated navigation. The proposed scheme can be well applied in the fields of aircraft and aerospace navigation.

For the SINS alignment phase, a fast initial alignment scheme is proposed: the initial value of reverse filter is determined by the final result of forward filter, and then, the reverse filter is carried out using the stored data. Multiple iterations are performed until the accuracy is satisfied. For the SINS-/GPS-integrated phase, a forward–reverse navigation algorithm is proposed: first, the standard forward filter is used, and then, the reverse filter is carried out using the initial value determined by the forward filter, and the final fusion results are achieved by the weighted smoothing of the forward and reverse filtering results.

The simulation and the actual test results show that in the initial alignment stage, the proposed reverse processing method can obviously shorten the SINS alignment time and improve the alignment accuracy. In the SINS-/GPS-integrated navigation data fusion stage, the proposed forward–reverse data fusion processing can, obviously, improve the performance of the navigation solution.

The proposed reverse processing technology has an important application in improving the accuracy of navigation and evaluating the performance of real-time navigation. The proposed scheme can be not only used for SINS-/GPS-integrated system but also applied to other integrated systems for general aviation aircraft.

Compared with the common forward filtering algorithm, the proposed reverse scheme can not only shorten alignment time and improve alignment accuracy but also improve the performance of the integrated navigation.

The paper aims to investigate theoretically and experimentally vibrational alignment of parts in an assembly position under kinematical excitement of the movably based part.

Presents developed mathematical model for vibrational alignment when the kinematical excitement of movable part is applied along the insertion axis. Dependencies of alignment duration on stiffness of basing elements and excitation frequency were defined numerically solving the mobile‐based part alignment equations. Alignment experiments of rectangular cross‐section and cylindrical parts under kinematical excitement were carried out.

The mathematical model and the experiments have demonstrated that alignment of the parts being assembled happens due to directed displacement of the movable part resulted by certain parameters of the system and excitement. In the course of the displacement, mating surfaces are aligned and the final mutual orientation of the parts before insertion is realized. Experiments have proved validity of the developed mathematical model. This process reduces allowable axial non‐coincidence and angular misfit of the parts to be assembled.

Impact and non‐impact regimes of the displacement exist depending on the excitement amplitude and initial contact force between the parts. Also, during the vibrational alignment it is possible to control dry friction force between parts by additional high frequency vibrations. Besides, the vibrational excitement can be not only harmonic, but also impulse, bi‐harmonic, etc. Only non‐impact regime of the motion without dry friction force control was investigated and presented in the paper.

The paper investigates the vibrational alignment method based on the directed vibrational displacement of the connecting part, which does not require high preciseness of the interdependent position of the parts in the assembly position.

Vibrational assembly devices of directional action enable compensation of errors of the parts' mutual positioning without use of sensors, feedback systems and control algorithms.

Market deregulation in the utilities sector has led to increased competition and rising customer expectations in both established and new markets. This, in turn, has forced organisations such as electricity and telecoms to make rapid, enterprise-wide changes on an increasingly frequent basis which in turn has led to problems with alignment. Misalignment can occur at many levels and can result in misused resources, loss of competitiveness, excessive cycle times, higher costs and loss of agility. The purpose of this paper is twofold. Given the lack of overarching theory, the paper begins by borrowing from contingency, dynamic capability and organisational learning constructs, to explore the role that performance measurement models can bring to improve the alignment between business strategy and functional strategy (level 1 alignment). Second, the paper analyses the role of performance measurement models in developing functional practices aligned with supply chain management (SCM) strategies (level 2 alignment).

The study adopts an exploratory theory-building approach using four case studies. These are used as key supply chains in both established and new business areas within two longitudinal university-industry research partnerships (each of three years duration). Data from repeat interviews (n=42), focus groups (n=10), documentation and observations is analysed and forms the basis for the development of a conceptual framework and a set of related propositions. The data analysis followed Radnor and Boaden's (2004) method for analysing interpretive research.

The findings show the role and impact of performance measurement models and methods on alignment at two levels, i.e. level 1 alignment – between business strategy and functional (SCM) strategy, and level 2 alignment – between the functional strategy (SCM) and SCM routines and practices.

To date, there are few studies which explore the development of theory and practice in relation to the role and impact of performance measurement models and methods in improving organisational alignment. This exploratory theory building study makes a contribution to this gap through the development of the conceptual framework and propositions.

The purpose of this paper is to present an optimal posture evaluation model to control the assembly gaps in aircraft wing assembly. The gaps between two mating surfaces should be strictly controlled in precision manufacturing. Oversizing of gaps will decrease the dimensional accuracy and may reduce the fatigue life of a mechanical product. To reduce the gaps and keep them within tolerance, the relative posture (orientation and position) of two components should be optimized in the assembly process.

Based on the step alignment strategy, i.e. preliminary alignment and refined alignment, the concept of a small posture transformation (SPT) is introduced. In the preliminary alignment, an initial posture is estimated by a set of auxiliary locating points, with which the components can be quickly aligned near each other. In the refined alignment, the assembly gaps are calculated and the formulation of the gaps with component posture is derived by the SPT. A comprehensive weighted minimization model with gap tolerance constraints is established for redistributing the gaps in multi-regions. Powell-Hestenes-Rockafellar optimization, Singular Value Decomposition and K-Dimensional tree searching are introduced for the solution of the optimal posture for localization.

Using the SPT, the trigonometric posture transformation is linearized, which benefits the iterative solution process. Through the constrained model, overall gaps are minimized and excess gaps are controlled within tolerance.

This method has been tested with simulated model data and real product data, the results of which have shown efficient coordination of mating components.

This paper proposed an optimal posture evaluation method for minimizing the gaps between mating surfaces through component adjustments. This will promote the assembly automation and variation control in aircraft wing assembly.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

The paper aims to explore how managers change their strategic view so that they come with a better understanding of the strategy. It uses two proxies for such understanding: balance in beliefs (taken from performance measurement system literature) and consensus on strategic priorities (taken from strategic literature).

A longitudinal study is conducted in a financial institution during a strategic change communicated through a tailor‐made balanced scorecard (BSC). The changes are measured in the degree of understanding experienced by a set of 45 middle managers in each of the two phases in which the BSC implementation has been divided. The paper tests to what extent as the BSC implementation progresses there is a balancing in users' beliefs, an increase in consensus and alignment of managers' priorities; and finally, whether or not those proxies of managers' understanding are interchangeable.

Results show that the implementation of this BSC brought about a change in managers' beliefs by increasing the importance given to measures located in the lower BSC perspectives (called balancing effect), as well as an increase in the degree of consensus on strategic priorities. However, in the paper more balance in managers' beliefs were not necessarily associated with a higher degree of consensus and alignment. The two proxies are not interchangeable and the balancing effect was found to be ineffective and insufficient in providing an explanation for the consensus formation process.

The paper provides empirical evidence on how middle managers change their mental models and improve their understanding of the strategy. The paper helps in aligning performance measurement systems literature and strategic literature.

In order to accomplish real‐time alignment of Shipborne strapdown inertial navigation system (SINS) on moving bases, a novel solution method of utilizing neural networks for rapid transfer alignment of Shipborne SINS was investigated.

The system error state equations and measurement equations of the Shipborne transfer alignment were established. Based on the nonlinear and time‐variant SINS model on moving bases, a neural network learning algorithm based on Kalman filtering was presented, and the methods of constructing and training of neural networks input‐output sample pairs suitable for Shipborne SINS were proposed.

Velocity and attitude errors between the master and slave inertial navigation system (INS) are chosen as network's inputs, and the information of sample pairs is affluent, which can advance the stability and generalization of the neural networks. The neural networks algorithms based on Kalman filtering not only have the self‐learning ability, but also remain recursive optimal estimation capability of Kalman filtering. Through the introducing of the local level trajectory frame, the trained neural networks can be independent on a ship heading, and only dependent on the relative position errors between master with slave INS and the inertial sensor errors.

This article presents an innovative solution method of utilizing neural networks for rapid transfer alignment of Shipborne SINS.