scholarly journals A Methodology for Industrial Robot Calibration Based on Measurement Sub-Regions

2021 ◽  
Author(s):  
Juan Sebastian Toquica ◽  
José Maurı́cio Motta
Keyword(s):  
Industrial Robot ◽  
Low Cost ◽  
Kinematic Model ◽  
Measurement Data ◽  
Industrial Applications ◽  
Industrial Robots ◽  
Robot Calibration ◽  
High Precision Measurement ◽  
Measurement Systems ◽  
Robot Accuracy

Abstract This paper proposes a methodology for calibration of industrial robots that uses a concept of measurement sub-regions, allowing low-cost solutions and easy implementation to meet the robot accuracy requirements in industrial applications. The solutions to increasing the accuracy of robots today have high-cost implementation, making calibration throughout the workplace in industry a difficult and unlikely task. Thus, reducing the time spent and the measured workspace volume of the robot end-effector are the main benefits of the implementation of the sub-region concept, ensuring sufficient flexibility in the measurement step of robot calibration procedures. The main contribution of this article is the proposal and discussion of a methodology to calibrate robots using several small measurement sub-regions and gathering the measurement data in a way equivalent to the measurements made in large volume regions, making feasible the use of high-precision measurement systems but limited to small volumes, such as vision-based measurement systems. The robot calibration procedures were simulated according to the literature, such that results from simulation are free from errors due to experimental setups as to isolate the benefits of the measurement proposal methodology. In addition, a method to validate the analytical off-line kinematic model of industrial robots is proposed using the nominal model of the robot supplier incorporated into its controller.

Study of Neural-Kinematics Architectures for Model-Less Calibration of Industrial Robots

2021 ◽  
Vol 33 (1) ◽  
pp. 158-171
Author(s):  
Monica Tiboni ◽  
◽  
Giovanni Legnani ◽  
Nicola Pellegrini
Keyword(s):  
Degrees Of Freedom ◽  
Industrial Robot ◽  
Kinematic Model ◽  
Back Propagation ◽  
Simulated Data ◽  
Industrial Robots ◽  
Robot Calibration ◽  
Feed Forward Neural Network ◽  
Comparative Performance ◽  
Order Of Magnitude

Modeless industrial robot calibration plays an important role in the increasing employment of robots in industry. This approach allows to develop a procedure able to compensate the pose errors without complex parametric model. The paper presents a study aimed at comparing neural-kinematic (N-K) architectures for a modeless non-parametric robotic calibration. A multilayer perceptron feed-forward neural network, trained in a supervised manner with the back-propagation learning technique, is coupled in different modes with the ideal kinematic model of the robot. A comparative performance analysis of different neural-kinematic architectures was executed on a two degrees of freedom SCARA manipulator, for direct and inverse kinematics. Afterward the optimal schemes have been identified and further tested on a three degrees of freedom full SCARA robot and on a Stewart platform. The analysis on simulated data shows that the accuracy of the robot pose can be improved by an order of magnitude after compensation.

scholarly journals Remote Reflectivity Sensor for Industrial Applications

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1301
Author(s):  
Federico Cavedo ◽  
Parisa Esmaili ◽  
Michele Norgia
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Industrial Applications ◽  
Interference Immunity ◽  
Surface Reflection ◽  
Measurement Systems ◽  
Digital Detector ◽  
Reflected Light ◽  
Surface Optical ◽  
Diffused Light

A low-cost optical reflectivity sensor is proposed in this paper, able to detect the presence of objects or surface optical properties variations, at a distance of up to 20 m. A collimated laser beam is pulsed at 10 kHz, and a synchronous digital detector coherently measures the back-diffused light collected through a 1-inch biconvex lens. The sensor is a cost-effective solution for punctual measurement of the surface reflection at different distances. To enhance the interference immunity, an algorithm based on a double-side digital baseline restorer is proposed and implemented to accurately detect the amplitude of the reflected light. As results show, the sensor is robust against ambient light and shows a strong sensitivity on a wide reflection range. The capability of the proposed sensor was evaluated experimentally for object detection and recognition, in addition to dedicated measurement systems, like remote encoders or keyphasors, realized far from the object to be measured.

Optimization of the Robot Calibration Process

1992 ◽  
Author(s):  
G. Zak ◽  
R. G. Fenton ◽  
B. Benhabib
Keyword(s):  
Optimization Problem ◽  
Kinematic Model ◽  
Optimization Procedure ◽  
Calibration Procedure ◽  
Residual Error ◽  
Industrial Robots ◽  
Multiple Objective Optimization ◽  
Robot Calibration ◽  
Calibration Process ◽  
The Cost

Abstract Most industrial robots cannot be off-line programmed to carry out a task accurately, unless their kinematic model is suitably corrected through a calibration procedure. However, proper calibration is an expensive and time-consuming procedure due to the highly accurate measurement equipment required and due to the significant amount of data that must be collected. To improve the efficiency of robot calibration, an optimization procedure is proposed in this paper. The objective of minimizing the cost of the calibration is combined with the objective of minimizing the residual error after calibration in one multiple-objective optimization. Prediction of the residual error for a given calibration process presents the main difficulty for implementing the optimization. It is proposed that the residual error is expressed as a polynomial function. This function is obtained as a result of fitting a response surface to either experimental or simulated sample estimates of the residual error. The optimization problem is then solved by identifying a reduced set of possible solutions, thus greatly simplifying the decision maker’s choice of an effective calibration procedure. An application example of this method is also included.

Determination of the Optimum Cost-Residual Error Trade-off in Robot Calibration

1994 ◽  
Vol 116 (1) ◽  
pp. 28-35 ◽  
Author(s):  
G. Zak ◽  
R. G. Fenton ◽  
B. Benhabib
Keyword(s):  
Kinematic Model ◽  
Optimization Procedure ◽  
Calibration Procedure ◽  
Residual Error ◽  
Industrial Robots ◽  
Multiple Objective Optimization ◽  
Robot Calibration ◽  
Optimum Cost ◽  
The Cost

Most industrial robots cannot be off-line programmed to carry out a task accurately, unless their kinematic model is suitably corrected through a calibration procedure. However, proper calibration is an expensive and time-consuming procedure due to the highly accurate measurement equipment required and due to the significant amount of data that must be collected. To improve the efficiency of robot calibration, an optimization procedure is proposed in this paper. The objective of minimizing the cost of the calibration is combined with the objective of minimizing the residual error after calibration in one multiple-objective optimization. Prediction of the residual error for a given calibration process presents the main difficulty for implementing the optimization. It is proposed that the residual error is expressed as a polynomial function. This function is obtained as a result of fitting a response surface to either experimental or simulated sample estimates of the residual error. The optimization problem is then solved by identifying a reduced set of possible solutions, thus greatly simplifying the decision maker’s choice of an effective calibration procedure. An application example of this method is also included.

scholarly journals Design of an Accurate and Stiff Wooden Industrial Robot: First Steps Toward Robot Eco-sustainable Mechanical Design

2020 ◽  
Vol 12 (5) ◽  
Author(s):  
Sébastien Briot ◽  
Lila Kaci ◽  
Clément Boudaud ◽  
David Llevat Pamiès ◽  
Pauline Lafoux ◽  
...  
Keyword(s):  
Design Process ◽  
Mechanical Design ◽  
Control Strategies ◽  
Industrial Robot ◽  
Integrated Design ◽  
Industrial Robots ◽  
Stiffness Ratio ◽  
Robot Accuracy ◽  
Integrated Design Process ◽  
Accuracy Performance

Abstract This article investigates the feasibility of replacing metal robot links by wooden bodies for eco-sustainable design’s purpose. Wood is a material with low environmental impact and a good mass-to-stiffness ratio. However, it has significant dimensional and mechanical variabilities. This is an issue for industrial robots that must be accurate and stiff. To guarantee stiffness and accuracy performance of a wooden robot, we propose an integrated design process combining (i) proper wood selection, (ii) adequate sensor-based control strategies to ensure robot accuracy, and (iii) a robust design approach dealing with wood uncertainties. Based on the use of this integrated design process, a prototype of a wooden five-bar mechanism is designed and manufactured. Experimental results show that it is realistic to design a wooden robot with performance compatible with industry requirements in terms of stiffness (deformations lower than 400 μm for 20 N loads) and accuracy (repeatability lower than 60 μm), guaranteed in a workspace of 800 mm × 200 mm. This study provides a first step toward the eco-sustainable mechanical design of robots.

scholarly journals Towards Safer Rides: Measuring Motorcycle Dynamics with Smartphones

2020 ◽  
Author(s):  
Maximilian Stanglmayr ◽  
◽  
Maximilian Bäumler ◽  
Keyword(s):  
Measurement System ◽  
Inertial Sensors ◽  
Road Traffic ◽  
Low Cost ◽  
Measurement Data ◽  
Smartphone App ◽  
High Precision Measurement ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Motorcycle Dynamics

Motorcyclists are among the most vulnerable road users in road traffic. Often, the cause of accidents is a loss of control on rural roads which could be averted by making use of the physical potential in terms of larger lean angles. At the same time, in reality driven lean angles over a larger group of riders and a longer route are unknown which is mainly due to the special measuring technology required. The focus is therefore on the development of a low-cost measurement method for measuring the lean angles of motorcycles. Smartphones are usually characterized by integrated inertial sensors, which are suitable for the acquisition of motorcycle driving dynamics. Employing a smartphone app tailored to the requirements for collecting measurement data on the motorcycle, the data of the sensors are recorded. During the offline evaluation, the rotation angles between the smartphone and the motorcycle coordinate system are determined, the inertial measurement data are transformed and the roll angle is calculated. An essential part is the alignment of the developed measurement chain with a high-precision measurement system. This was carried out on different routes and thus the data quality was determined. As a feasibility study, a test person study with several participants was carried out, which confirmed the practical suitability of the measurement chain. Hence, the study outcomes are briefly shown and discussed. The successful validation on different routes, the practical suitability of the data acquisition and the accuracy of the measurement system encourage to roll out the smartphone app to a larger panel of test persons and thus to collect data on a larger driver collective.

scholarly journals Compensation for absolute positioning error of industrial robot considering the optimized measurement space

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092164
Author(s):  
Junde Qi ◽  
Bing Chen ◽  
Dinghua Zhang
Keyword(s):  
Industrial Robot ◽  
Measurement Data ◽  
Joint Space ◽  
Industrial Robots ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Decoupling Method ◽  
Absolute Positioning ◽  
Motion Characteristics ◽  
High Flexibility

Industrial robots are getting widely applied due to their low use-cost and high flexibility. However, the low absolute positioning accuracy limits their expansion in the area of high-precision manufacturing. Aiming to improve the positioning accuracy, a compensation method for the positioning error is put forward in terms of the optimization of the experimental measurement space and accurate modelling of the positioning error. Firstly, the influence of robot kinematic performance on the measurement accuracy is analysed, and a quantitative index describing the performance is adopted. On this basis and combined with the joints motion characteristics, the optimized measurement space in joint space as well as Cartesian space is obtained respectively, which can provide accurate measurement data to the error model. Then the overall model of the positioning error is constructed based on modified Denavit–Hartenberg method, in which the geometric errors and compliance errors are considered comprehensively, and an error decoupling method between them is carried out based on the error-feature analyses. Experiments on the KUKA KR210 robot are carried out finally. The mean absolute positioning accuracy of the robot increases from 1.179 mm to 0.093 mm, which verifies the effectiveness of the compensation methodology in this article.

Fast, Accurate, and Automated 3D Reconstruction Using a Depth Camera Mounted on an Industrial Robot

2021 ◽  
Author(s):  
Rishi Malhan ◽  
Rex Jomy Joseph ◽  
Prahar M. Bhatt ◽  
Brual Shah ◽  
Satyandra K. Gupta
Keyword(s):  
3D Reconstruction ◽  
Cycle Time ◽  
Industrial Robot ◽  
Low Cost ◽  
Point Clouds ◽  
Industrial Robots ◽  
Depth Camera ◽  
Reconstruction Process ◽  
Depth Cameras ◽  
Manufacturing Applications

Abstract 3D reconstruction technology is used in a wide variety of applications. Currently, automatically creating accurate pointclouds for large parts requires expensive hardware. We are interested in using low-cost depth cameras mounted on commonly available industrial robots to create accurate pointclouds for large parts automatically. Manufacturing applications require fast cycle times. Therefore, we are interested in speeding up the 3D reconstruction process. We present algorithmic advances in 3D reconstruction that achieve a sub-millimeter accuracy using a low-cost depth camera. Our system can be used to determine a pointcloud model of large and complex parts. Advances in camera calibration, cycle time reduction for pointcloud capturing, and uncertainty estimation are made in this work. We continuously capture point-clouds at an optimal camera location with respect to part distance during robot motion execution. The redundancy in pointclouds achieved by the moving camera significantly reduces errors in measurements without increasing cycle time. Our system produces sub-millimeter accuracy.

Analysis of non-geometric accuracy effects of articulated robots

2017 ◽  
Vol 44 (5) ◽  
pp. 639-647
Author(s):  
Gregor Lux ◽  
Marco Ulrich ◽  
Thomas Baker ◽  
Martin Hutterer ◽  
Gunther Reinhart
Keyword(s):  
Joint Stiffness ◽  
Industrial Applications ◽  
Industrial Robots ◽  
Kinematic Structure ◽  
Geometric Accuracy ◽  
Robot Calibration ◽  
Content Type ◽  
Articulated Robots ◽  
Non Destructive ◽  
Geometric Effects

Purpose Articulated robots are widely used in industrial applications owing to their high repeatability accuracy. In terms of new applications such as robot-based inspection systems, the limitation is a lack of pose accuracy. Mostly, robot calibration approaches are used for the improvement of the pose accuracy. Such approaches however require a profound understanding of the determining effects. This paper aims to provide a non-destructive analysis method for the identification and characterisation of non-geometric accuracy effects in relation to the kinematic structure for the purpose of an accuracy enhancement. Design/methodology/approach The analysis is realised by a non-destructive method for rotational, uncoupled robot axes with the use of a 3D lasertracker. For each robot axis, the lasertracker position data for multiple reflectors are merged with the joint angles given by the robot controller. Based on this, the joint characteristics are determined. Furthermore, the influence of the kinematic structure is investigated. Findings This paper analyses the influence of the kinematic structure and non-geometric effects on the pose accuracy of standard articulated robots. The provided method is shown for two different industrial robots and presented effects incorporate tilting of the robot, torsional joint stiffness, hysteresis, influence of counter balance systems, as well as wear and damage. Practical implications Based on these results, an improved robot model for a better match between the mathematical description and the real robot system can be achieved by characterising non-geometric effects. In addition, wear and damages can be identified without a disassembly of the system. Originality/value The presented method for the analysis of non-geometric effects can be used in general for rotational, uncoupled robot axes. Furthermore, the investigated accuracy influencing effects can be taken into account to realise high-accuracy applications.

Application of the Weighted Least Squares Parameter Estimation Method to the Robot Calibration

1994 ◽  
Vol 116 (3) ◽  
pp. 890-893 ◽  
Author(s):  
G. Zak ◽  
B. Benhabib ◽  
R. G. Fenton ◽  
I. Saban
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Kinematic Model ◽  
Measurement Data ◽  
Estimation Method ◽  
Kinematic Parameter ◽  
Measuring System ◽  
Parameter Estimates ◽  
Robot Calibration

Significant attention has been paid recently to the topic of robot calibration. To improve the robot’s accuracy, various approaches to the measurement of the robot’s position and orientation (pose) and correction of its kinematic model have been proposed. Little attention, however, has been given to the method of estimation of the kinematic parameters from the measurement data. Typically, a least-squares solution method is used to estimate the corrections to the parameters of the model. In this paper, a method of kinematic parameter estimation is proposed where a standard least-squares estimation procedure is replaced by weighted least-squares. The weighting factors are calculated based on all the a priori available statistical information about the robot and the pose-measuring system. By giving greater weight to the measurements made where the standard deviation of the noise in the data is expected to be lower, a significant reduction in the error of the kinematic parameter estimates is made possible. The improvement in the calibration results was verified using a calibration simulation algorithm.

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