Automated fruit harvesting is crucial for alleviating labor shortages and enhancing agricultural productivity. In this context, it is crucial to obtain information on fruit poses before picking in order to avoid damaging the fruit and/or the plant. However, the complex and unstructured orchard environment poses significant challenges regarding the pose estimation task. In this study, a dragon fruit pose estimation (DFPE) framework using a single RGB image is proposed for dragon fruit automated harvesting, which includes three key components: dataset annotation processing, keypoint detection, and geometric pose estimation. First, a multi-source dataset consisting of 8467 images is constructed to enhance the estimation model’s generalizability. A pseudo four-keypoint annotation strategy is designed to fit the annotation rules of mainstream single-class keypoint detection models and mitigate the inherent limitations of multi-target keypoint detection in agricultural scenarios. This strategy implicitly encodes the fruit’s orientation using bounding box group IDs, while preserving geometric information for pose inference. Then, the fruit body and its two core keypoints (navel and stem) are detected via a real-time keypoint detection model. Notably, the proposed DFPE framework is detector-agnostic: other mainstream keypoint detection models can also be plugged into the subsequent geometric pose inference stage, which guarantees the generality and scalability of the framework. Finally, a dragon fruit pose estimation algorithm based on customized geometric constraints is designed, which takes the detected pose information as the input and outputs the posture of dragon fruit. The results of experiments conducted in natural orchard and laboratory environments demonstrate that the ellipses fitted using the proposed DFPE framework closely aligned with fruit contours, even under foliage occlusion conditions. In the laboratory environment, roll errors reached a maximum of 14.8°, whereas yaw errors peaked at 13.4°. Crucially, all roll and yaw errors remained consistently below 15°, which is well within the tolerance threshold required for non-destructive picking operations using a harvesting robot. In summary, this work presents a low-cost solution for dragon fruit pose estimation from a single RGB image, which can potentially be extended to other ellipsoid crops and is suitable for implementation in harvesting robots operating in orchards.

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines.

Attitude control of unmanned aerial vehicles is a problem that needs to be solved in a reliable manner. The research presented in this paper examines a systematic approach to the design of an LQR state feedback controller for the three-DOF hover system. The state space model is used to derive the feedback gain K, with the diagonal elements of the weighting matrices Q and R used as design variables. A multi-objective grey wolf optimizer is used to obtain Q-R matrices based on closed-loop simulations under representative roll, pitch and yaw reference commands. There are four separate multi-objective optimization runs, each using one of four standard error indices which are the integral of absolute error (IAE), the integral of time-weighted absolute error (ITAE), the integral of squared error (ISE) and the integral of time-weighted squared error (ITSE). Each index is used to track roll, pitch and yaw errors at the same time and the resulting non-dominated solution sets are post-processed using TOPSIS to select a compromise knee-point design. The simulation results show that the adjusted LQR parameters lead to feasible tracking performance. The proposed framework provides a systematic and replicable method for LQR weight selection in hover-type attitude control problems under the considered simulation settings.

Rotational modulation improves strapdown inertial navigation system (SINS) by periodically reorienting the inertial measurement unit (IMU) to convert slowly varying sensor errors into manageable, cancelable components. However, existing dual-axis schemes may accumulate large total rotation angles and introduce delayed error balancing, which results in non-negligible residual attitude errors and degrades real-time navigation accuracy. To overcome these limitations, we propose an odd-symmetric dual-axis rotation strategy that jointly optimizes the rotation order and dwell positions to maximize error cancellation on each axis and across axes while constraining cumulative rotation. Based on this principle, we design a 64-position rotation scheme and derive its IMU error modulation/suppression characteristics, including gyroscope drift, accelerometer bias, scale-factor errors, and misalignment (installation) errors, and we quantify their effects on attitude and velocity. Simulations show that the proposed scheme reduces position and velocity errors by more than 60% compared to a 16-position scheme, and decreases longitude error, east-velocity error, and yaw error by more than 30% relative to a 32-position scheme. Experiments further validate consistent improvements in position, velocity, and attitude accuracy, demonstrating the effectiveness of the proposed rotational design for dual-axis SINS.

Abstract This paper presents an analysis of mobility on an over-actuated Remotely Operated Vehicle (ROV) that becomes under-actuated due to multiple actuator faults of different types of degradation. The analysis is developed on a regulation control problem, including modeling and robust control design. The dynamic model is formulated using a Port-Hamiltonian formalism, emphasizing energy conservation and passivity. The control strategy is developed through a Control by Interconnection (CbI) structure to regulate the ROV posture as an interconnected energy-based system. Singular Value Decomposition (SVD) is employed to assess mobility effectiveness in each spatial direction. Quadratic Programming (QP) is used for realistic control allocation under actuator constraints and fault conditions, highlighting the direction with reduced effectiveness for improving performance in a complete posture regulation task. The simulation results validate control allocation based on QP, demonstrating its robustness in maintaining ROV stability and achieving posture regulation under various fault scenarios. It has been shown that the ROV topology under study supports two individual thruster faults and still achieves a 3D target, and the resulting reduced directions can be enhanced by QP allocation; for instance, yaw error is improved by nearly 50% compared with the SVD method, demonstrating its ability to redistribute control forces efficiently while preserving closed-loop stability. Furthermore, computational performance analysis confirms the approach’s real-time feasibility for experimental implementation, with the QP solver operating at about 1 ms per iteration.

Yaw angle errors, resulting from wind direction measurement deviations and control inaccuracies, prevent individual turbines in a wind farm from achieving their optimal yaw positions. Such yaw uncertainties not only affect the aerodynamic stability of individual turbines but also propagate through wakes to downstream turbines, causing significant fluctuations in the overall aerodynamic performance of the wind farm. It is therefore essential to investigate the mechanisms by which yaw angle uncertainty impacts turbine and wind farm performance and to develop effective strategies to mitigate these uncertainties. In this paper, considering yaw angle uncertainty for multiple turbines, we investigate the global power uncertainty and the propagation law of turbine power uncertainty within the wind farm. A wind farm aerodynamic performance optimization method is proposed and compared with traditional methods. Results show that for the optimized wind farm, the mean value of the median of the power interval increases by 1.4%, and the interval radius of the power output decreases by 29% on average. The proposed methodology can also be extended to other engineering control devices, enhancing robustness against uncertainties and increasing reliability.

Dual-LiDAR systems are widely deployed in autonomous driving, yet extrinsic calibration remains challenging in non-overlapping field-of-view (FoV) configurations where correspondence-based methods are unreliable. We propose an engineering-oriented 2.5D calibration framework that estimates horizontal extrinsics (x,y,yaw) via motion-guided planar alignment and then refines them using Gaussian Process Implicit Surfaces (GPIS), which provide continuous and probabilistic surface constraints from spatially disjoint scans. This design avoids calibration targets and reduces dependence on strong scene assumptions, improving robustness under noise and weak structure. Extensive high-fidelity simulation experiments demonstrate centimeter-level lateral accuracy and sub-degree yaw error, consistently outperforming representative motion-based and BEV-based baselines under both clean and noisy settings. To further assess real-world applicability, we conduct a preliminary nuScenes case study by splitting LiDAR scans into front and rear subsets to emulate a non-overlapping dual-LiDAR setup, achieving improved yaw accuracy and competitive lateral precision. Overall, the proposed method serves as a practical refinement stage for non-overlapping dual-LiDAR calibration, with a favorable balance of accuracy, robustness, and engineering feasibility.

This article investigates an integrated approach of fault diagnosis and initial alignment of redundant strapdown inertial navigation systems (SINSs) under large misalignment angles. A redundant configuration of four hemispherical resonator gyroscopes (HRGs) and four accelerometers is designed. The parity vector method combined with generalized likelihood ratio test is developed for reliable detection and identification of HRG bias faults. For initial alignment, an analytic coarse alignment provides an initial attitude estimate, which is followed by a precise alignment phase using an unscented Kalman filter (UKF). The UKF is specifically designed to handle the nonlinear error model associated with large yaw misalignment angles. Experimental results demonstrate that the proposed fault diagnosis method effectively identifies HRG faults. Furthermore, comparative studies show that while the UKF and extended Kalman filter yield similar performance for small misalignment angles, the UKF achieves significantly superior alignment accuracy, especially under large yaw misalignment angle. This integrated approach enhances system reliability and navigation precision, which achieves a 100% detection rate for the tested bias faults and reduces the yaw error from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20^{\circ }$</tex-math></inline-formula> to below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.35^{\circ }$</tex-math></inline-formula>.

To address the problems of slow path planning speed, high path cost, and visual positioning errors encountered by grain harvesters during field operations, this study proposes an improved rapidly-exploring random tree algorithm integrated with visual servoing (VS-IRRT). By employing visual servoing technology to acquire environmental information in real time, the algorithm enables accurate positioning and attitude correction of the harvester. On this basis, heuristic sampling strategies and a path optimization function are introduced to enhance node expansion efficiency and accelerate the convergence of the search tree. To further reduce path cost, a path evaluation model incorporating environmental feature costs is established, which comprehensively considers terrain complexity, crop distribution density, and the machine’s turning radius. This model dynamically adjusts the search direction and improves path smoothness. Simulation and field navigation experiment results indicate that the VS-IRRT algorithm reduces path planning time by approximately 32% compared to the traditional RRT algorithm, decreases the average yaw error by 42%, reduces the path curvature variation rate by 33%, and lowers turning frequency by 21%. The algorithm also maintains high robustness and planning accuracy under visual noise and positioning disturbances. Overall, this study provides an effective path planning approach and technical support for autonomous navigation and efficient operation of grain harvesters in complex agricultural environments.

The global wind power industry is increasingly adopting large-scale, deep-water offshore facilities, such as floating offshore wind turbines (FOWTs), which are more susceptible to typhoon impacts. Characterizing the dynamic response of FOWTs to typhoons is therefore essential for ensuring their structural safety. This study assesses the dynamic responses of the International Energy Agency 15 Megawatt (MW) FOWT under Typhoon Hagupit using Open-source Fatigue, Aerodynamics, Structures, and Turbulence. The investigation focuses on the coupled effects of yaw error and wind-wave misalignment (WWM), and the impacts of the typhoon's multi-stage evolution characteristics. Key findings include: (1) Among the five stages of typhoon passage, the front eyewall stage induces the most critical dynamic loads and responses on the FOWT. (2) Power spectral densities of platform motions reveal intricate coupling mechanisms. (i) Response coupling: surge and pitch: wave-driven within the 0.05–0.15 Hertz (Hz) band; sway and roll: wind-driven at 0.007 Hz. (ii) Structural coupling: platform pitch ↔ 1st tower fore-and-aft (FA) mode (0.524 Hz); platform sway/roll ↔ first tower side-to-side (SS) mode (0.535 Hz). (3) Yaw error and WWM under typhoons strongly amplify loads, especially the yaw error. Relative to the baseline (0° yaw error and 0° WWM), the maximum coupled effects increase the blade edgewise moment up to 26–35-fold. Overall, the yaw error should ideally be maintained within ±5°; the identified structure coupling highlights the importance of avoiding resonant interactions during the design phase. These analyses provide critical insights for anti-typhoon design, yaw-control optimization, and coupled-load mitigation under typhoons.

This study presents a novel adaptive model predictive control (AMPC) framework for robust lateral motion tracking in semi-autonomous vehicles operating under dynamically uncertain conditions. Traditional controllers often struggle to maintain stability and accuracy in the presence of nonlinear vehicle dynamics, time-varying parameters, and external disturbances. The proposed AMPC system integrates real-time parameter estimation via recursive least squares with a predictive optimization structure, allowing continuous adaptation to variations in vehicle mass, speed, and tire-road friction. A comprehensive simulation environment developed in MATLAB/Simulink was used to evaluate the AMPC across multiple scenarios, including aggressive lane changes, crosswind disturbances, and low-friction conditions (friction coefficient μ = 0.4). Quantitative comparisons with conventional model predictive control (MPC) and linear quadratic regulator (LQR) controllers demonstrate that AMPC achieves a 43% reduction in lateral tracking error and a 37% improvement in yaw angle root mean square error (RMSE), maintaining peak yaw errors below 0.275 radians even under severe disturbances. Steering control signals remain smooth and within actuator limits, with maximum steering rates under 0.48 rad/s. Additionally, a human-in-the-loop simulation confirmed the controller’s ability to handle delayed driver intervention (1–3 s) without compromising vehicle stability or trajectory tracking. These results validate AMPC’s superior robustness, adaptability, and real-time performance in managing complex lateral control tasks. The framework provides a scalable solution for enhancing safety and reliability in shared-control driving environments, addressing both technical and human-centric aspects of advanced driver assistance systems (ADAS).

Linear guides are fundamental components of high-end precision equipment, and their geometric errors directly affect the measurement and machining accuracy. To achieve efficient and accurate measurement of geometric motion errors in linear guides, this paper proposes a 6-DOF simultaneous measurement method that integrates heterodyne interferometry, collimation/autocollimation, and polarization principles. To address the degradation of straightness measurement accuracy under long-distance conditions caused by air turbulence, a dual-wavelength laser-based compensation method is developed to suppress turbulence-induced beam deviation. A turbulence compensation model based on a dual-wavelength proportional cancellation principle is established, and its effectiveness is verified through COMSOL (v6.3) simulations and experimental studies. Experimental results show that the proposed approach significantly outperforms the traditional simple moving-average (SMA) filter. It improves straightness measurement stability by more than 56%. Under a 3200 mm measurement range and ordinary laboratory conditions, the repeatabilities (k = 2) of the 6-DOF motion-error measurements are 6.4 μm for positioning error, 6.4 μm and 5.5 μm for straightness errors, 1.7″ and 2.1″ for yaw and pitch errors, and 4.3″ for roll error. The proposed method exhibits high measurement accuracy and robustness, making it suitable for simultaneous 6-DOF motion-error measurement of long linear guides.

This study proposes a PID-Difuso controller to enhance stabilization of the DJI Tello Edu drone, addressing limitations of conventional PID controllers in dynamic environments. Traditional PID controllers exhibit rigidity when confronted with external disturbances (wind, mass variations) and the inherent nonlinearity of UAVs, resulting in residual oscillations and tracking errors. The proposed solution integrates a two-layer hybrid architecture: • Fuzzy layer: dynamically adjusts PID gains (K_p, K_i, K_d) through heuristic rules based on angular error (e) and its derivative (ė), employing Mamdani inference with triangular membership functions. • PID layer: executes the control law with real-time adaptive parameters. Autonomous flight experiments—including straight trajectories and 180° turns—demonstrated significant improvements over the conventional PID: • RMS yaw error reduced by 23.1%. • MAE yaw error reduced by 25.9%. • Settling time decreased by 28.6%. • Energy consumption decreased by up to 13.1%, extending flight autonomy. • Maximum yaw error during critical maneuvers reduced by 44.4%. The hybrid controller optimizes the trade-off between precision and adaptability. Validation under controlled conditions utilized 20 Hz WiFi telemetry and standardized metrics (RMSE, MAE, control energy). Implementation on a low-cost platform such as the DJI Tello Edu democratizes access to advanced control systems for education and research.

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (&lt;0.44°2), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization.

Given the inevitability of yaw error in wind turbine operation, this study effectively integrates computational fluid dynamics (CFD) simulations with analytical models to investigate its impact on wind farm performance. The CFD results demonstrate the necessity of accounting for yaw error effects in wind engineering practice, as it can significantly alter wake interference and thereby affect wind farm power output. In a neutral boundary layer, a 15° yaw error angle can lead to a change in wind farm efficiency exceeding 20%. To address the need for rapid and accurate power prediction for wind farms affected by yaw error in engineering practice, a novel analytical model is proposed, which incorporates the transverse velocity effects critical to wake interference among yawed turbines. Comparisons with CFD data from simulations involving 35 National Renewable Energy Laboratory 5 MW wind turbines under various yaw angle configurations reveal that the mean absolute percentage error (MAPE) calculated using the new model remains consistently below 5%, substantially lower than the conventional model's MAPE of up to 20.82%. Parametric studies based on the newly proposed model further indicate that the impact of yaw error on wind farm efficiency is highly sensitive to changes in turbine orientation and can be mitigated by increasing incoming turbulence intensity or expanding the wind farm size.

In order to address the problem of error accumulation in long-duration autonomous navigation using Strapdown Inertial Navigation Systems (SINS), this paper proposes an error prediction and correction method based on Deep Neural Networks (DNN). A 12-dimensional feature vector is constructed using angular increments, velocity increments, and real-time attitude and velocity states from the inertial navigation system, while a 9-dimensional response vector is composed of attitude, velocity, and position errors. The proposed DNN adopts a feedforward architecture with two hidden layers containing 10 and 5 neurons, respectively, using ReLU activation functions and trained with the Levenberg–Marquardt algorithm. The model is trained and validated on a comprehensive dataset comprising 5 × 103 seconds of real vehicle motion data collected at 100 Hz sampling frequency, totaling 5 × 105 sample points with a 7:3 train-test split. Experimental results demonstrate that the DNN effectively captures the nonlinear propagation characteristics of inertial errors and significantly outperforms traditional SINS and LSTM-based methods across all dimensions. Compared to pure SINS calculations, the proposed method achieves substantial error reductions: yaw angle errors decrease from 2.42 × 10−2 to 1.10 × 10−4 radians, eastward velocity errors reduce from 455 to 4.71 m/s, northward velocity errors decrease from 26.8 to 4.16 m/s, latitude errors reduce from 3.83 × 10−3 to 7.45 × 10−4 radians, and longitude errors reduce dramatically from 3.82 × 10−2 to 1.5 × 10−4 radians. The method also demonstrates superior performance over LSTM-based approaches, with yaw errors being an order of magnitude smaller and having significantly better trajectory tracking accuracy. The proposed method exhibits strong robustness even in the absence of external signals, showing high potential for engineering applications in complex or GPS-denied environments.

Collision avoidance technology provides an effective solution for improving vehicle driving safety. However, current research primarily focuses on passenger cars and small commercial vehicles, neglecting tractor semi-trailers, which have elongated body lengths and complex dynamic characteristics. There is still a significant research gap in the application of collision avoidance control technology for tractor semi-trailers. To address this, this paper proposes a safe trajectory planning and tracking strategy for steering collision avoidance scenarios, aimed at enhancing the driving safety of tractor semi-trailers. To tackle the challenge of accurately predicting the pose of the semi-trailer during collision avoidance trajectory planning, a real-time trajectory planning method combining model predictive control and artificial potential field is proposed. Then, a tracking error model considering both lateral and yaw errors of the tractor semi-trailer is established to address the off-tracking phenomenon, and a linear quadratic regulator control strategy is proposed. Finally, static and dynamic collision avoidance scenarios are designed to validate the proposed strategy. Simulation and experimental results show that the proposed control strategy effectively ensures the safe collision avoidance maneuver of the tractor semi-trailer.

Yaw errors occur in wind turbines either during the installation stage or because of the aging of devices. It reduces the wind speed in the rotor axial direction and increases the structural loads in the lateral direction. Diagnosing yaw error rapidly and accurately is crucial for avoiding the introduced under-performance. In this review paper, we first introduce the fundamental concepts and principles of wind turbine yaw control strategies, and we discuss two types of yaw errors (i.e., the static yaw error and the dynamic yaw error) with their corresponding causes. Subsequently, we outline the existing yaw error diagnostic methods, which are based on the LiDAR (light detection and ranging) data, the SCADA (supervisory control and data acquisition) data, or a combination of the two, and we discuss the advantages and disadvantages of various methods. At last, we emphasize that the diagnostic performance can be improved via the combination of the LiDAR data and the SCADA data, and it benefits from an in-depth understanding of the salient features and influential factors associated with the yaw error. Meanwhile, the potential of intelligent clusters and digital twins for detecting yaw errors is discussed.

Robots have made significant contributions across various industries due to their efficiency and effectiveness. However, indoor navigation remains challenging due to complex environments and sensor signal interference. Changes in indoor conditions and the limited range of GPS signals necessitate the development of an accurate and efficient indoor robot navigation system. This study aims to create an autonomous indoor navigation system for medical robots using sensors such as Marvelmind, LiDAR, IMU, and an odometer, along with the Time Elastic Band (TEB) local planning algorithm to detect dynamic obstacles. The algorithm’s performance is evaluated using metrics like path length, duration, speed smoothness, path smoothness, Mean Squared Error (MSE), and positional error. In the test arena, TEB demonstrated superior efficiency with a path length of 155.55 m, 9.83 m shorter than the Dynamic Window Approach (DWA), which covered 165.38 m, and had a lower yaw error of 0.012 radians. TEB outperformed DWA in terms of speed smoothness, path smoothness, and MSE. In the Sterile Room Arena, TEB had an average path length of 14.84 m, slightly longer than DWA’s 14.32 m, but TEB navigated 2.82 s faster. Additionally, TEB showed better speed and path smoothness. In the Obstacle Room Arena, TEB recorded an average path length of 21.96 m in 57.3 s, outperforming DWA, which covered 23.44 m in 61 s, with better results in MSE, speed smoothness, and path smoothness, highlighting superior path consistency. These findings indicate that the TEB algorithm is an effective choice as a local planner in dynamic hospital environments.

The assembly accuracy of motion axis of machine tool is determined by multi parameters, including geometric errors of linear guides and parallelism between multi axis, named error equalization effect. This article proposes a multi-functional laser calibrator, which is consisted of a novel five degree of freedom measurement (5-DOFM) system and a dual-axis precision level with a pentagonal prism. The geometric errors of linear guide (including straightness errors, pitch, yaw and roll angular errors) and parallelism between multi axis could be detected conveniently. Especially, the influence of the pentagonal prism on parallelism measurement was investigated and proved using reflection matrices. A series of validation experiments were conducted to demonstrate the effectiveness of laser calibrator. The experiments results reveal that designed laser calibrator can achieve measurement range of ± 150 um for straightness errors detection, and ± 200″ for angular errors detection. The measurement error of parallelism between multi guide rails can be controlled less than 1″ (5 um/m) according the analysis results of the pentagonal prism. The proposed instrument can provide numerical reference for the research of motion axis error equalization effect and the improvement of assembly accuracy.