Position Error Research Articles

Navigation in GNSS-Denied Environment Based on Observation of RSOs and Stars

Navigating in an environment, where Global Navigation Satellite System (GNSS) signals are unavailable, presents a formidable challenge. A feasible strategy involves integrating stellar observations with inertial information to realize precise navigation. Inertial/Stellar Celestial Navigation enhances position accuracy by addressing gyro drift through stellar observations. However, the position error still continues to grow over time which arises from unobservable error factors. With improved orbit determination accuracy and the resident space object (RSO) observation method, navigating using RSO observation is gaining viability. This study models the RSO navigation problem as a Perspective-n-Point (PnP) problem and proposes that observing more than 6 RSOs can yield complete solution for position and attitude. Additionally, a tightly coupled navigation algorithm integrating inertial, stellar, and RSO observations is introduced. Simulation validation under dynamic condition demonstrates the algorithm’s efficacy, achieving a position accuracy of 30[Formula: see text]m and an attitude accuracy of 5[Formula: see text]arcsec (RMS) without GNSS data.

TransSMPL: Efficient Human Pose Estimation with Pruned and Quantized Transformer Networks

Existing Transformers for 3D human pose and shape estimation models often struggle with computational complexity, particularly when handling high-resolution feature maps. These challenges limit their ability to efficiently utilize fine-grained features, leading to suboptimal performance in accurate body reconstruction. In this work, we propose TransSMPL, a novel Transformer framework built upon the SMPL model, specifically designed to address the challenges of computational complexity and inefficient utilization of high-resolution feature maps in 3D human pose and shape estimation. By replacing HRNet with MobileNetV3 for lightweight feature extraction, applying pruning and quantization techniques, and incorporating an early exit mechanism, TransSMPL significantly reduces both computational cost and memory usage. TransSMPL introduces two key innovations: (1) a multi-scale attention mechanism, reduced from four scales to two, allowing for more efficient global and local feature integration, and (2) a confidence-based early exit strategy, which enables the model to halt further computations when high-confidence predictions are achieved, further enhancing efficiency. Extensive pruning and dynamic quantization are also applied to reduce the model size while maintaining competitive performance. Quantitative and qualitative experiments on the Human3.6M dataset demonstrate the efficacy of TransSMPL. Our model achieves an MPJPE (Mean Per Joint Position Error) of 48.5 mm, reducing the model size by over 16% compared to existing methods while maintaining a similar level of accuracy.

Gaussian–Student’s t Mixture Distribution-Based Robust Kalman Filter for Global Navigation Satellite System/Inertial Navigation System/Odometer Data Fusion

Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively.

Real-Time Operational Trial of Atmosphere–Ocean–Wave Coupled Model for Selected Tropical Cyclones in 2024

An atmosphere–ocean–wave coupled regional model, the UWIN-CM, began its operational trial in real time at the Hong Kong Observatory (HKO) in the second half of 2024. Its performance in the analysis of three selected tropical cyclones, Severe Tropical Storm Prapiroon, Super Typhoon Gaemi, and Super Typhoon Yagi, are studied in this paper. The forecast track and intensity of the tropical cyclones were verified against the operational analysis. It is shown that the track error of the UWIN-CM was lower than other regional numerical weather prediction (NWP) models in operation at the HKO, with a reduction in mean direct positional error of up to 50% for the first 48 forecast hours. For cyclone intensity, the performance of the UWIN-CM was the best out of the available global and regional models at HKO for Yagi at forecast hours T + 36 to T + 84 h. The model captured the rapid intensification of Yagi over the SCS with a lead time of 24 h or more. The forecast winds were compared with the in situ measurements of buoy and with the wind field analysis obtained from synthetic-aperture radar (SAR). The correlation of forecast winds with measurements from buoy and SAR ranged between 65–95% and 50–70%, respectively. The model was found to perform generally satisfactorily in the above comparisons.

Systemic Effects of Molar and Incisor Biting on Walking Direction With and Without Visual Feedback

Gait stability and walking direction control are conventionally attributed to coordination among somatosensory, visual, and vestibular systems. Recent evidence of functional interdependence between masticatory and neuromuscular systems indicates that the stomatognathic system is neurologically integrated with various body systems relevant to movement planning and execution. This study investigated the effects of unilateral molar biting and incisor biting on walking with and without visual feedback. A cohort of 31 healthy young adults aged 21 to 30 years (average age of 23.93 ± 1.89) participated in this study. Three types of errors in walking direction (angle error, position error, and curve error) were computed. Our findings indicate that, in right-handed individuals, irrespective of visual feedback, unilateral biting caused systematic deviations toward the biting side from initiation to termination of walking. The consistent deviation in walking, particularly during unilateral right biting conditions in right-handed individuals, may indicate a complex interplay between masticatory function and gait control mechanism, potentially influenced by handedness and motor lateralization within the cortex. This study establishes a foundation for future research exploring the interrelation between bite location, visual feedback, and motor control in diverse populations. This research may provide insight for more efficient interventions for gait-related disorders.

Decreased isometric neck strength is a risk factor for head, neck and face injuries in professional rugby league players

ABSTRACT Head, neck and face injuries are a concern in contact sports. This exploratory study aimed to establish 1) injury risk factors for head, neck and face injuries and 2) clinical cut-off values related to strength, endurance and proprioception of the cervical spine in a team of professional rugby league players. Pre-season assessments of isometric strength of the flexor, extensor and lateral flexor muscles, endurance of the flexor muscles and joint position error were conducted. Injuries resulting in games missed were recorded. Cross-tabulations were used to determine the unadjusted odds ratios for the measures as risk factors for playing season injuries. The unadjusted odds ratio (OR) values indicated that if a player had weaker extensors of the neck (<36.4 kg, p = 0.014; <3.4N/kg, p = 0.014) or asymmetry of isometric strength of their lateral flexor muscles (left-to-right ratio <0.91, p = 0.005), their odds of games missed due to season head, neck and face injuries was increased (OR extensors = 8; 95% CI = 1.5-42.5 OR asymmetry of lateral flexor muscles OR = 12.6; 95% CI = 2.0-79.4). As muscle strength is modifiable, the clinical application of this study would involve targeting players in the team beneath the clinical cut-off value.

Performance of a fine-scale acoustic positioning system for monitoring temperate fish behavior in relation to offshore marine developments

Rapid global expansion of offshore wind farms, tidal, and wave technologies signifies a new era of renewable energy development. While a promising means to combat the impacts of climate change, such developments necessitate fine-scale monitoring of biological communities to determine impacts associated with construction, operation, and eventual decommission. Here, we evaluate the performance of a gridded, Innovasea Systems, Inc. fine-scale acoustic telemetry positioning system (FSPS, n = 20 acoustic receivers) for tracking behaviors of diverse, temperate fish assemblages in relation to a subsea cable route supporting the Ørsted offshore wind development in coastal New York. We examined array performance through positioning error derived from receiver reference transmitters and tracked animals (n = 260) comprising 17 species of teleost and elasmobranch. We evaluated the effects of environmental variables (temperature, tilt, noise, and depth), transmitter power, individual movement rates, and receiver loss on horizontal positioning error (HPE) and route mean squared error (RMSE). Across a 16-month deployment period, many positions were derived for Atlantic sturgeon (n = 2,612), black sea bass (n = 9,175), clearnose skate (n = 10,306), summer flounder (n = 13,304), and little skate (n = 15,186), suggesting that these species may serve as sentinel candidates for assessing behavioral changes following construction, operation, and decommission. We found that receivers placed at the boundary of the grid exhibited higher HPE and RMSE, however these errors did not significantly change despite large receiver losses (25%). Generalized Linear Models revealed that temperature, noise, tilt, and depth were often significant predictors of HPE and RMSE, however, a substantial amount of variance was not explained by the models (~ 70%). Average movement rates ranged from 1.1 m s−1 (common thresher shark) to 0.03 m s−1 (little skate and summer flounder) but had minimal effects on positioning error. Finally, we observed that higher transmitter powers (158 dB) may lead to higher and more variable HPE values. Overall, these findings provide new insight into the drivers of FSPS array performance and illustrate their broad utility for monitoring fish behavior associated with offshore marine developments.

Calibration of Spatial Position Errors in Virtual and Real Spaces: Improving the Results of AR-Guided Wiring

Augmented Reality (AR) enables an immersive understanding of industrial operating behavior in a mixed environment of reality and virtual. Hence, an AR-guided navigation system for industrial wiring is proposed. The objective is to establish an interactive learning platform for the digital transmission of wiring skills to improve efficiency and quality. Firstly, the mapping relationship from virtual to real is constructed, and a reasonable spatial error threshold is set to reduce the error. Then, the visual positioning method is used to calibrate the spatial position errors of virtual parts to optimize the AR guidance effect. Finally, the virtual operation is accurately mapped to the real space for guidance. The results show that AR-guided wiring reduces operating time by 22% and errors by 74% compared to real wiring. Compared to general AR-guided wiring, the calibrated wiring exhibits an 80% reduction in operating errors. As a result, the virtual and real spatial calibration contributes to efficient AR-guided wiring for accurate digital training.

Improving the position accuracy via merged line and point features for GNSS/SINS/Vision integration in a degradation environment

Abstract In complex urban environments, the GNSS/INS integration diverges over time when severe GNSS degradation occurs due to the harsh environment. Based on visual point features, the visual-inertial odometry (VIO) system has been integrated with the GNSS to solve this problem. However, the accuracy of this system decreases when the GNSS and visual point features degrade simultaneously in the GNSS harsh environments and low-texture scenes. The artificial structures in complex urban environments render line features more reliable than point features. To further improve the positioning accuracy of the point-based fusion system, we propose a GNSS/INS/Vision integration method based on point and line features, where visual measurements in a sliding window and the GNSS position are used to reduce or eliminate the cumulative errors of the IMU state in the back end. In addition, visual features are merged at the front end via an adaptive threshold based on the line feature length. Two urban infield experiments are used to evaluate the effectiveness of the proposed method through improvements in the time consumption of line feature matching and position accuracy. The analysis demonstrates that the average time consumption is reduced by 50.03% and 82.19%, and the maximum position error is reduced by 42.56% and 55.48%, respectively in the two scenarios. Additional evaluations based on KAIST Urban 38 public datasets indicate that our proposed system achieves a better positioning accuracy than that of VINS-Fusion, which is a state-of-the-art GNSS/INS/Vision integration system that uses only point features.

Tightly Coupled SLAM Algorithm Based on Similarity Detection Using LiDAR-IMU Sensor Fusion for Autonomous Navigation

In recent years, the rise of unmanned technology has made Simultaneous Localization and Mapping (SLAM) algorithms a focal point of research in the field of robotics. SLAM algorithms are primarily categorized into visual SLAM and laser SLAM, based on the type of external sensors employed. Laser SLAM algorithms have become essential in robotics and autonomous driving due to their insensitivity to lighting conditions, precise distance measurements, and ease of generating navigation maps. Throughout the development of SLAM technology, numerous effective algorithms have been introduced. However, existing algorithms still encounter challenges, such as localization errors and suboptimal utilization of sensor data. To address these issues, this paper proposes a tightly coupled SLAM algorithm based on similarity detection. The algorithm integrates Inertial Measurement Unit (IMU) and LiDAR odometry modules, employs a tightly coupled processing approach for sensor data, and utilizes curvature feature optimization extraction methods to enhance the accuracy and robustness of inter-frame matching. Additionally, the algorithm incorporates a local keyframe sliding window method and introduces a similarity detection mechanism, which reduces the real-time computational load and improves efficiency. Experimental results demonstrate that the algorithm achieves superior performance, with reduced positioning errors and enhanced global consistency, in tests conducted on the KITTI dataset. The accuracy of the real trajectory data compared to the ground truth is evaluated using metrics such as ATE (absolute trajectory error) and RMSE (root mean square error).

Directional Spectrum Estimation for Sea States Generated by the Single Summation Method

The influence of directional spreading of waves is significant for wave-induced loads, wave breaking and nonlinearity of the waves. For physical model testing performed at test facilities such as the Ocean and Coastal Engineering Laboratory at Aalborg University, it is crucial to validate if the test conditions match the target sea states by measurement and analysis of the generated directional wave field. Most of the existing methods assumes a double summation sea state to be present which is valid in the prototype. However, waves in the laboratory are usually generated by single summation. The current paper presents a method to analyse short-crested waves generated by the single summation method. Compared to similar methods oblique reflections are considered instead of only in-line reflections. The results show that the method successfully decomposes the incident and reflected wave fields in the time domain. Thus, for example the incident wave height distribution may be obtained. The sensitivity of the new method to additional reflective directions, noise, calibration errors and positional errors of the wave gauges was found small.

Control-response characteristics of deceleration braking system of pipeline intelligent plugging robot

Controlling pipeline intelligent plugging robots (PIPRs) to execute rapid and precise deceleration braking within a designated distance is pivotal for enhancing the efficiency of oil-and-gas pipeline maintenance and repair operations. Therefore, for a PIPR that relies on friction braking between the rubber cylinder and pipe wall, a hydraulic control system for deceleration braking is designed to achieve rapid and precise deceleration braking, and an experimental setup is established to verify its feasibility. Concurrently, a joint simulation model of constant deceleration nonlinear dynamics based on the fuzzy Proportion Integration Differentiation (PID) algorithm is proposed to elucidate the effects of key parameters, such as the initial velocity and braking distance, on the stability of the dynamic control of the robot during deceleration braking. The results show that the designed hydraulic control system effectively achieves deceleration braking. The regulation time increases as the initial speed of the robot decreases. The error of the deceleration braking distance ranges from 0.3 to 0.5 m, with reduced positioning and steady-state errors. Under varying deceleration braking distances, the maximum acceleration overshoot is −3.54 m/s2, and increasing the deceleration braking distance effectively reduces the positioning error. This study offers theoretical and empirical support for investigating PIPRs.

Ultrasound-assisted aberration correction of transcranial photoacoustic imaging based on angular spectrum theory

To correct the refraction aberration induced by the skull in photoacoustic imaging, a method for phase distortion compensation is proposed based on the angular spectrum theory with the aid of ultrasonic signals. This method first updates the speed of sound distribution by iteratively performing aberration correction in the ultrasonic reconstruction. Then the speed of sound distribution obtained with ultrasound-assisted serves as prior knowledge to address phase distortion compensation by adjusting the phase shift factor of the wavefront in different media. Finally, the aberration-corrected ultrasonic-photoacoustic dual-modality image can be obtained. Numerical simulations and phantom experiments confirm the effectiveness of this method. Specifically, in simulations, the position error of the proposed method is reduced from −13.61% to 1.27% in depth compared to the method based on the reconstruction with constant speed. Moreover, a real ex-vivo rabbit skull experiment illustrates the potential biological application of the proposed method in transcranial photoacoustic imaging.

Analysis of the Position Error and Displacement Parameters of Ideal and Real Mechanisms for Robotic Systems

Analysis of the Position Error and Displacement Parameters of Ideal and Real Mechanisms for Robotic Systems

Extending higher-order model for non-conservative perturbing forces acting on Galileo satellites during eclipse periods

For precise orbit determination (POD) and precise applications with POD products, one of the critical issues is the modeling of non-conservative forces acting on satellites. Since the official publication of Galileo satellite metadata in 2017, analytical models including the box-wing model and thermal thrust models have been established to absorb a substantial amount of solar radiation pressure (SRP) and thermal thrust. These models serve as the foundation for the best overall modeling approach, combining the analytical box-wing model and thermal thrust model with parameterization of the remaining non-conservative perturbing forces using various optimized Empirical CODE Orbit Models (ECOMs) of the Center for Orbit Determination in Europe (CODE). Firstly, we have demonstrated the significance of the second-order signals in the D direction and the first-order signals in the B direction through spectral analyses of the pure box-wing model, which are consistent with the currently recommended 7-parameter Empirical CODE Orbit Model 2 (ECOM2). In spite of this, we still found that degradation in orbit accuracy frequently occurs during deep eclipse seasons when using the ECOM2 model. We confirm a high-frequency signal existing in the fluctuating orbit overlap differences through the spectral analysis. Considering this, the ECOM2 force model should be extended to higher order and adapted to absorb the remaining effects of potential perturbing forces. After extending the ECOM2 force model to the sixth order in the Sun direction, we demonstrated the significance of fourth- and sixth-order sine terms for deep eclipses. Due to the higher-order periodic terms, the averaged RMS values of orbit overlap difference over deep eclipses can be reduced from 5.3, 10.8, and 23.8 cm to 3.2, 3.9, and 9.9 cm for in-orbit validation (IOV) satellites, from 5.0, 8.6, and 17.7 cm to 3.0, 3.0, and 7.1 cm for the first generation of full operational capability (FOC-1) satellites, and from 5.4, 8.6, and 19.0 cm to 3.6, 3.6, and 7.4 cm for the second generation of FOC (FOC-2) satellites, in the radial, cross-track, and along-track directions, respectively. Fluctuations with a peak amplitude of approximately 0.4 nm/s2 in the bias in the solar panel axis (Y) direction (Y-bias) are effectively mitigated by the higher-order terms. Due to the higher-order terms, the vertical positioning errors during kinematic precise point positioning (PPP) convergence can be improved from 42.3 to 37.1 cm at the 95.5% confidence level. Meanwhile, a low correlation level of up to 0.02 is found between the newly introduced higher-order parameters and earth rotation parameters (ERPs).

A deep learning-enabled visual-inertial fusion method for human pose estimation in occluded human-robot collaborative assembly scenarios

In the context of human-centric smart manufacturing, human-robot collaboration (HRC) systems leverage the strengths of both humans and machines to achieve more flexible and efficient manufacturing. In particular, estimating and monitoring human motion status determines when and how the robots cooperate. However, the presence of occlusion in industrial settings seriously affects the performance of human pose estimation (HPE). Using more sensors can alleviate the occlusion issue, but it may cause additional computational costs and lower workers' comfort. To address this issue, this work proposes a visual-inertial fusion-based method for HPE in HRC, aiming to achieve accurate and robust estimation while minimizing the influence on human motion. A part-specific cross-modal fusion mechanism is designed to integrate spatial information provided by a monocular camera and six Inertial Measurement Units (IMUs). A multi-scale temporal module is developed to model the motion dependence between frames at different granularities. Our approach achieves 34.9 mm Mean Per Joint Positional Error (MPJPE) on the TotalCapture dataset and 53.9 mm on the 3DPW dataset, outperforming state-of-the-art visual-inertial fusion-based methods. Tests on a synthetic-occlusion dataset further validate the occlusion robustness of our network. Quantitative and qualitative experiments on a real assembly case verified the superiority and potential of our approach in HRC. It is expected that this work can be a reference for human motion perception in occluded HRC scenarios.

Cycle slip detection and repair method towards multi-frequency BDS-3/INS tightly coupled integration in kinematic surveying

Carrier phase integer ambiguities must be determined for BDS-3/inertial navigation system (INS) tightly coupled (TC) integration to achieve centimetre-level positioning accuracy. However, cycle slip breaks the consistency of the integer ambiguities. Conventional multi-frequency cycle slip methods use the pseudorange; thus, requiring improvement when applied to kinematic situations. Furthermore, a concise and nonprior information-dependent model is crucial for real-time processing. In this study, an inertial-aided BDS-3 cycle slip detection and repair (I-CDR) method was developed. First, a BDS-3/INS TC model with I-CDR was created. The ionospheric delays were modelled as part of the TC states; therefore, they could be estimated and eliminated. Investigations were conducted on the effects of carrier phase noise, residual ionosphere delay, and INS-predicted position error on combined cycle slip detection (CCD) accuracy. The optimal CCDs under various frequency available configurations were determined. The effectiveness of I-CDR was demonstrated using land vehicle test data. The false alarm ratio was less than 1.0%, and the missed detection ratio was almost zero even in situations with challenging abundant 1-cycle slips in random epochs. Furthermore, the right determination ratio reached 100%. In addition, BDS-3 signal loss-recovery cases were simulated, and all cycle slips for all satellites could be repaired within 40s. I-CDR exhibits outstanding cycle slip detection and repair performance for dense 1-cycle slip and signal loss-recovery cases, demonstrating its suitability for BDS-3/INS TC integration.

A Novel Algorithm for Precise Orbit Determination Using a Single Satellite Laser Ranging System Within a Single Arc for Space Surveillance and Tracking

A satellite laser ranging (SLR) system uses lasers to measure the range from ground stations to space objects with millimeter-level precision. Recent advances in SLR systems have increased their use in space surveillance and tracking (SST). The problem we are addressing, the precise orbit determination (POD) using one-dimensional range observations within a single arc, is challenging owing to infinite solutions because of limited observability. Therefore, general orbit determination algorithms struggle to achieve reasonable accuracy. The proposed algorithm redefines the cost value for orbit determination by leveraging residual tendencies in the POD process. The tendencies of residuals are quantified as R-squared values using Fourier series fitting to determine velocity vector information. The algorithm corrects velocity vector errors through the grid search method and least squares (LS) with a priori information. This approach corrects all six dimensions of the state vectors, comprising position and velocity vectors, utilizing only one dimension of the range observations. Simulations of three satellites using real data validate the algorithm. In all cases, the errors of the two-line element data (three-dimensional position error of 1 km and velocity error of 1 m/s, approximately) used as the initial values were reduced by tens of meters and the cm/s level, respectively. The algorithm outperformed the general POD algorithm using only the LS method, which does not effectively reduce errors. This study offers a more efficient and accurate orbit determination method, which improves the safety, cost efficiency, and effectiveness of space operations.

Efficient and Accurate Force Replay in Cosmological-baryonic Simulations

We construct time-evolving gravitational potential models for a Milky Way–mass galaxy from the FIRE-2 suite of cosmological-baryonic simulations using basis function expansions. These models capture the angular variation with spherical harmonics for the halo and azimuthal harmonics for the disk, and the radial or meridional plane variation with splines. We fit low-order expansions (four angular/harmonic terms) to the galaxy’s potential for each snapshot, spaced roughly 25 Myr apart, over the last 4 Gyr of its evolution, then extract the forces at discrete times and interpolate them between adjacent snapshots for forward orbit integration. Our method reconstructs the forces felt by simulation particles with high fidelity, with 95% of both stars and dark matter, outside of self-gravitating subhalos, exhibiting errors ≤4% in both the disk and the halo. Imposing symmetry on the model systematically increases these errors, particularly for disk particles, which show greater sensitivity to imposed symmetries. The majority of orbits recovered using the models exhibit positional errors ≤10% for 2–3 orbital periods, with higher errors for orbits that spend more time near the galactic center. Approximate integrals of motion are retrieved with high accuracy even with a larger potential sampling interval of 200 Myr. After 4 Gyr of integration, 43% and 70% of orbits have total energy and angular momentum errors within 10%, respectively. Consequently, there is higher reliability in orbital shape parameters such as pericenters and apocenters, with errors ∼10% even after multiple orbital periods. These techniques have diverse applications, including studying satellite disruption in cosmological contexts.

Meta-learning enhanced adaptive robot control strategy for automated PCB assembly

The assembly of printed circuit boards (PCBs) is one of the standard processes in chip production, directly contributing to the quality and performance of the chips. In the automated PCB assembly process, machine vision and coordinate localization methods are commonly employed to guide the positioning of assembly units. However, occlusion or poor lighting conditions can affect the effectiveness of machine vision-based methods. Additionally, the assembly of odd-form components requires highly specialized fixtures for assembly unit positioning, leading to high costs and low flexibility, especially for multi-variety and small-batch production. Drawing on these considerations, a vision-free, model-agnostic meta-method for compensating robotic position errors is proposed, which maximizes the probability of accurate robotic positioning through interactive feedback, thereby reducing the dependency on visual feedback and mitigating the impact of occlusions or lighting variations. The proposed method endows the robot with the capability to learn and adapt to various position errors, inspired by the human instinct for grasping under uncertainties. Furthermore, it is a self-adaptive method that can accelerate the robotic positioning process as more examples are incorporated and learned. Empirical studies show that the proposed method can handle a variety of odd-form components without relying on specialized fixtures, while achieving similar assembly efficiency to highly dedicated automation equipment. As of the writing of this paper, the proposed meta-method has already been implemented in a robotic-based assembly line for odd-form electronic components. Since PCB assembly involves various electronic components with different sizes, shapes, and functions, subsequent studies can focus on assembly sequence and assembly route optimization to further enhance assembly efficiency.