Mojtaba Ahmadieh Khanesar

Surface normal estimation is a fundamental step in many applications, including robotics, manufacturing inspection and quality control. Reliable surface normal information is essential for accurate surface reconstruction, defect detection, and geometric analysis. In this paper, different approaches for estimating surface normal vectors from point cloud data are investigated, with particular focus on their suitability for generating ground-truth surface normals. Such ground-truth measurements are required to evaluate and validate surface normal estimation techniques used in practical metrology and 3D sensing systems in accordance with established surface texture standards such as ISO 25178. The analysis focuses on methods based on neighbourhood clustering followed by a surface normal fitting procedure. In addition, the influence of point cloud noise on the accuracy and stability of the estimated normals is systematically studied. By comparing the performance of different estimation strategies under different noise conditions, their suitability for generating reliable reference surface normals is investigated. The results demonstrate how point cloud characteristics and noise levels affect the robustness of surface normal estimation and highlight the advantages and limitations of commonly used approaches. The proposed analysis provides practical guidance for selecting appropriate methods when generating ground-truth surface normals for the validation of optical and geometric measurement systems. To show that the studied approaches can be used for surface normal estimation independent of metrology equipment, they are applied to the point clouds acquired from laser scanners, confocal optical measurement equipment, coherence scanning interferometers, and fringe projection systems with a variety of test objects. The results show that the studied approaches can estimate the surface normals. Highly accurate surface normal estimation may ease surface texture studies. The findings contribute to improving the reliability of point cloud analysis and surface normal estimation in manufacturing metrology and related applications.

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values.

A virtual instrument has been developed to study a multi-illumination dome system on digital objects using a camera mounted on top of the dome. The reliability of the blender environment is investigated for simulating the functions of components of this system. The high-precision three-dimensional (3D) mesh of an object measured with a laser tracker equipped with an AS1 scanner is used as a digital object within this virtual instrument. The camera is modelled by both intrinsic and extrinsic parameters and the virtual lights by power, beam angle and diffusion. The parameters are adjusted to match the real camera and light sources. The surface normals estimated using images taken of the 3D digital object using different illumination conditions are compared against actual surface normals for three artefacts with fully determined surface normals. Results show that the three sets of surface normals match each other and the mean value of the angles between the sets of surface normals for least squares with a threshold approach is limited to a few degrees for the case of objects of a few centimeters in size. Inspired by the results of surface normal estimation for the three digital artefacts, the same algorithms are then applied to a digital object scanned using a laser scanner.

Highly accurate positioning of industrial robots is crucial to performing industrial operations with high quality. This paper presents a mechanical modification to an industrial robot aiming at enhancing the system actuation resolution, thereby enhancing its positional accuracy. The industrial robot under consideration is a six-degrees of freedom (DoF) robot with revolute joints. By integrating a linear stage, a prismatic joint is introduced to the robot’s end effector, reconfiguring it into a 7 DoF system with more precise step size capabilities. To improve the positional accuracy of the overall system, a closed-loop control structure is chosen. Positional feedback is provided using an industrial laser tracker. Initially, a multi-layer perceptron neural network (MLPNN) is used to identify the forward kinematics (FK) of the overall 6RP robotic system. The FK of the industrial robot using the pretrained MLPNN is then used online to compute the real-time sensitivity of positional error to changes in the joint angle values of the industrial robot and displacements of the prismatic joint. Different trajectories are used to test the accuracy of the proposed positioning algorithm. From the implementation results obtained using the proposed control structure, it is observed that the accuracy of the industrial robot improves significantly. Statistical results for five different points selected from the ISO 9283 trajectory over 30 times of measurements show an 82% improvement for the measurements using the proposed approach as compared to the original industrial robot controller.

Precision object handling and manipulation require the accurate positioning of industrial robots. A common practice for performing end effector positioning is to read joint angles and use industrial robot forward kinematics (FKs). However, industrial robot FKs rely on the robot Denavit–Hartenberg (DH) parameter values, which include uncertainties. Sources of uncertainty associated with industrial robot FKs include mechanical wear, manufacturing and assembly tolerances, and robot calibration errors. It is therefore necessary to increase the accuracy of DH parameter values to reduce the impact of uncertainties on industrial robot FKs. In this paper, we use differential evolution, particle swarm optimization, an artificial bee colony, and a gravitational search algorithm to calibrate industrial robot DH parameters. A laser tracker system, Leica AT960-MR, is utilized to register accurate positional measurements. The nominal accuracy of this non-contact metrology equipment is less than 3 μm/m. Metaheuristic optimization approaches such as differential evolution, particle swarm optimization, an artificial bee colony and a gravitational search algorithm are used as optimization methods to perform the calibration using laser tracker position data. It is observed that, using the proposed approach with an artificial bee colony optimization algorithm, the accuracy of industrial robot FKs in terms of mean absolute errors of static and near-static motion over all three dimensions for the test data decreases from its measured value of 75.4 μm to 60.1 μm (a 20.3% improvement).

This paper proposes a calibration algorithm to improve the positional accuracies of an industrial XY-linear stage. Precision positioning of these linear stages is required to maintain highly accurate object handling and manipulation. However, due to imprecisions in linear motor stages and the gearbox, static and dynamic errors exist within these manipulators that cannot be adjusted internally. In this paper, to improve the positioning accuracy of these manipulators, measurements from a laser tracker are used within an interval type-2 fuzzy logic system. The laser tracker used in this experiment is an AT960-MR, which is a highly accurate noncontact coordinate metrology equipment capable of performing highly accurate robotic measurements. To perform calibration, we use an IT2FLS to find a nonlinear correcting relationship to compensate for position errors. The IT2FLS acts on the commands given to the move stage to find the accurate position of the move stage. To train the IT2FLS, we use particle swarm optimization (PSO) for the antecedent part parameters and Moore–Penrose generalized inverse to estimate the consequent part parameters. Data are split into train/test data to test the efficacy of the proposed algorithm. It is shown that by using the proposed IT2FLS-based calibration approach, the standard deviation of the position errors can be decreased from 86.1μm to 55.9μm, which is a 35.1% improvement. Comparison results with a multilayer perceptron neural network reveal that the proposed IT2FLS-based calibration algorithm outperforms multilayer perceptron neural network for positional calibration purposes.

Static friction modeling is a critical task to have the accurate robot model. In this article, a neural network separation approach to include nonlinear static friction in models of industrial robots is proposed. For this purpose, the terms corresponding to static friction within the overall robot mathematical model are separable terms treated independently from the rest of the model. The separation modeling process is accomplished by first determining the mathematical model for the system by excluding the friction terms and estimating its parameter values. This part of the model corresponds to gravitational terms only. Because persistency of excitation is required to maintain high accuracy and avoid singularity in the estimations, data with large variations across multiple joint angles are gathered for estimation purposes and a weighted least squares approach is used. This estimation results in a highly accurate static mathematical model for industrial robots. Results from the weighted least squares estimation are compared with the original least squares estimation, ridge regression, a least absolute shrinkage and selection operator, and an elastic net to show superior performance. After modeling the gravitational terms of the model, a multilayer perceptron neural network is used to identify static friction forces in the model from the experimental data. This is required in the case of a robot with multiple degrees of freedom because the friction of each joint is a function of several other joint angles acting upon it; making the solution complex and difficult to be obtained through other friction modeling methods. The experimental results obtained from a Universal Robots-UR5 demonstrate the high accuracy of the proposed modeling methodology under static conditions, and future work will consider the implementation of dynamic terms to integrate friction forces during movement.

This paper presents a sliding mode fuzzy control approach for industrial robots at their static and near static speed (linear velocities less than 5 cm/s). The extended Kalman filter with its covariance resetting is used to translate the coordinates from Cartesian to joint angle space. The translated joint angles are then used as a reference signal to control the industrial robot dynamics using a sliding mode fuzzy controller. The stability and robustness of the proposed controller is proven using an appropriate Lyapunov function in the presence of parameter uncertainty and unknown dynamic friction. The proposed controller is simulated on a 6-DOF industrial robot, namely the Universal Robot-UR5, considering the maximum allowable joint torques. It is observed that the proposed controller can successfully control UR5 under uncertainties in terms of unknown dynamic friction and parameter uncertainties. The tracking performance of the proposed controller is compared with that of the sliding mode control approach. The simulation results demonstrate superior performance of the proposed approach over the sliding mode control method in the presence of uncertainties.