Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-8· 0 citations· 13 references
Abstract
This study presents a data-driven approach to improve the absolute positional accuracy of a MABI MAX-100-2.25-P industrial robot. To quantify positioning errors, the robot was programmed to move through a dense grid of points with spacing of 25 mm in two planes above and across a working table. A training dataset was acquired by pulling commanded positions from the robot controller and pairing them with tool centre point values measured by a Leica AT960-LR laser tracker. The end effector orientation was fixed programmatically throughout the experiment.Four predictive models were trained on the dataset to learn the error patterns between the commanded and measured tool centre point positions. These models were Linear Regression, Random Forest, Gradient Boosting, and Artificial Neural Networks. Whilst training and validation metrics initially indicated that Random Forest and Gradient Boosting offered the lowest mean squared errors (as low as 0.0 mm2), real-world testing revealed significant discrepancies in model generalisation. The models were evaluated on three distinct test sets: hold-out grid points, interpolated cell midpoints, and randomly generated points. In the tests, Linear Regression delivered the most robust performance, achieving Euclidean error reductions of up to 85% across all sets. In contrast, the tree-based models generalised poorly when given unseen or interpolated locations, sometimes even amplifying positional errors by over 3000%. Artificial Neural Network models demonstrated moderate generalisation capability, performing better than tree-based models on non-grid data but not surpassing Linear Regression. In the most widely applicable test, random points, Linear Regression achieved an average Euclidean error of 0.12 mm which corresponds to a repeatability to accuracy ratio of 1:2.4, an improvement on the 1:3 ratio from the robot datasheet.
This work addresses the problem of increasing the temporal resolution of robot positioning in outdoor inspection tasks by leveraging high-frequency inertial measurements. A learning-based approach is proposed to estimate incremental displacement from IMU data combined with GNSS/RTK positioning, using data collected along a predefined trajectory with a mobile robotic platform. Two neural architectures, LSTM and Transformer, are evaluated under different data preparation strategies. Offline validation shows that variations in hyperparameters have limited impact on performance, while the adopted data representation plays a more significant role, with high-resolution IMU–GNSS alignment outperforming feature-based approaches. The selected models were deployed on the robotic platform and tested in the same environment used for data collection, demonstrating real-time operation at the IMU sampling rate and achieving mean errors of approximately 0.140 m and 0.113 m for LSTM and Transformer, respectively. These results indicate that the proposed approach can enhance positioning update rates and support real-time motion estimation in robotic inspection tasks.
José Mario Nishihara de Albuquerque, João Henrique Campos Soares, Giovanni Braglia et al.· 2026 IEEE/ASME International...· 0 citations
Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.
T. Q. Le, T. Luu· IAES International Journal o...· 0 citations
A data-driven framework for predicting repeatability of FANUC LR Mate 200iD and KUKA KR 6 R700 Sixx robots under varying operational conditions provides a practical tool for predicting repeatability, supporting process planning, uncertainty budgeting, and precision manufacturing optimization.
B. Louhichi, Mohamed Slamani, I. Bonev et al.· Machines· 0 citations
Robotic analysis uses mathematical modeling and digital techniques to represent and study mechanical, electrical, and computational systems. Combining these models with physical robots enhances their functions, and artificial intelligence adds intelligence, enabling robots to perform more complex tasks. Inverse dynamics is a key component, mapping joint positions, velocities, and accelerations to the torques needed for accurate motion. This study presents a unified framework that integrates inverse dynamics learning, adaptive control, multi objective optimization, stability verification, and sensorless torque estimation for a 6 DOF UR5 manipulator. A structured dataset representing robot motion with controlled noise and uncertainty conditions is used to train three learning models: Deep Neural Networks, Type 2 Fuzzy Systems, and Gaussian Process Regression. The results show that Gaussian Process Regression achieves the highest torque prediction accuracy, with RMSE of 7.87 Nm and R2 of 0.892, closely followed by the Deep Neural Network (RMSE 11.90 Nm, R2 0.753). Integration with an adaptive controller reduces the trajectory tracking error to 0.0473 rad, which is further reduced to 0.0238 rad using Particle Swarm Optimization, alongside a 5.41% reduction in control energy. Stability analysis using Lyapunov and frequency domain methods confirms bounded, stable closed-loop tracking behaviour, while sensorless torque estimation improves prediction accuracy by 70%. The proposed framework provides a clear methodological foundation for learning-based inverse dynamics modelling and its integration with robotic control systems, and future work will extend the framework to real-time implementation on physical robotic systems.
A data engine which gathers data and improves its performance while executing the task, and demonstrates the ability to learn and reduce the need for expensive verifications over time, while staying within the set error-rate.
Zebin Duan, Norbert Krüger, Juan Heredia et al.· arXiv.org· 0 citations
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