Implementing AI models to correct static positioning and setup errors in an accurate industrial robot

Learning-Based Motion Estimation for Autonomous Inspection Robots: Temporally Aligned IMU–RTK Data Fusion

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.

Experimental validation of vision-based hybrid navigation for differential-drive robots

Reliable mobile-robot deployment requires mapping, localization, global planning, and real-time obstacle avoidance to operate consistently under sensor noise and physical constraints. This study presents a vision-based hybrid navigation framework for a differential-drive mobile robot in a warehouse-like environment. The main contribution lies in the system-level integration and experimental validation of established techniques rather than the development of a new standalone navigation algorithm. A ceiling-mounted camera converts top-view images into an occupancy-grid map and world coordinates, while a convolutional neural network (CNN) recognizes goal markers. Odometry–augmented reality University of Cordoba (ArUco) fusion is used to correct accumulated localization drift. Particle swarm optimization (PSO) generates smooth global paths offline, whereas the dynamic window approach (DWA) performs real-time local motion control. Experiments in a 3.3 m × 2.4 m workspace achieved average obstacle-localization errors of 0.959 cm and 0.696 cm along the x- and y-axes, respectively, and a goal-recognition accuracy of 99%. The DWA controller required 12.61±2.42 ms per cycle, while rapidly-exploring random tree (RRT) and PSO required 4–7 s and 477–692 s, respectively, for global path generation. 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.

Data-Driven Modeling of Industrial Robot Repeatability Using Ensemble Artificial Neural Networks Under Varying Operational Conditions

The positional repeatability of industrial robots is a critical yet state-dependent performance metric, highly sensitive to thermal conditioning and mechanical loading. This study develops a data-driven framework for predicting repeatability of FANUC LR Mate 200iD (FANUC, Oshino-mura, Japan) and KUKA KR 6 R700 Sixx (KUKA AG, Augsburg, Germany) robots under varying operational conditions. ISO 9283-compliant experiments using a TriCal system (TRI-CAL Ltd., Montreal, QC, Canada) were conducted across three warm-up durations, three payload levels, and five poses. Ensemble artificial neural networks with 10 independently trained networks were developed for each robot. The FANUC model achieved R2 = 0.9922, RMSE = 0.004231 mm, and MAE = 0.002979 mm, while the KUKA model achieved R2 = 0.9926, RMSE = 0.002919 mm, and MAE = 0.002215 mm. Prediction interval coverage was 93.3% for FANUC and 100% for KUKA. Per-pose R2 ranged from 0.9588 to 0.9966 for KUKA. Response surface analysis identified thermal stabilization as the dominant factor affecting repeatability, with improvements of 86% for FANUC and 84% for KUKA after 4 h of warm-up. The KUKA robot demonstrated superior robustness and lower variability compared to the FANUC robot. The framework provides a practical tool for predicting repeatability, supporting process planning, uncertainty budgeting, and precision manufacturing optimization.

Data-Driven and Sensor-Based Spatial Error Characterisation and Correction for Multi-Axis Robots

A systematic AI-based approach for inverse dynamics modelling and validation on UR5 manipulator

Bootstrapping Self-Supervised Learning of Binary Classification Using Error Bounds: A Case Study on a Robotic Insertion Task

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.