ABB IRB Research Articles

Identification of physically consistent dynamics parameter of the ABB IRB 360-6/1600 delta robot and its use for time-optimal motion planning under consideration of constraint forces

Model-based control schemes, forward dynamics simulations, constraint force computation and time-optimal motion planning have one major thing in common, they all depend on the dynamics parameters of the system. Physical consistency of the dynamics parameters ensures a positive definite mass matrix and correct constraint forces. The most common inverse dynamics identification method – the base-parameters – lack physical consistency. This paper proposes an identification method to identify physically consistent dynamics parameters for Delta-like robots while further showing the effects of friction in passive joints. A tailored model to compute the crucial constraint forces appearing in the mechanism based on the identified dynamics parameters is derived. This model is used to additionally consider constraint forces besides actuation torques for time-optimal motion planning of a typical pick and place task. This is done without any prior CAD data of the robot from the manufacturer.

Regression Model for the Prediction of Total Motor Power Used by an Industrial Robot Manipulator during Operation

Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson’s correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving R¯2=0.99924,σ=0.00007 and MA¯PE=0.33589,σ=0.00955, the model trained on the original, non-pruned, data achieves R¯2=0.98796,σ=0.00081 and MA¯PE=0.46895,σ=0.05636. These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation.

Computing The Kinematics Study of a 6 DOF Industrial Manipulator Prototype By Matlab

Since the mechanical parts in the robot are designed to do the movement, studying and analyzing the motion considered a primary issue that should be taken into consider when studying and designing the robot.
 In this research 6-DOF sample of the industrial manipulator based on ABB IRB 4400 model had been studied. The mathematical model of manipulator is established by DH (Denavait Hartenberg) method. The forward kinematics was done using DH Parameters in order to get the final transformation matrix. The inverse kinematics was done using geometrical and analytical methods in order to get the end effector final position and direction by calculate Euler angles values.
 Finally, the forward and inverse kinematics equations were computing by MATLAB to get angles, end effector, position, direction and Euler angles values.
 The kinematics study and the arm movement’s equations were compered with the practical measurements to make sure it fulfills the desired purpose.

Calibration strategies for enhancing accuracy in serial industrial robots for orbital milling applications

PurposeThis research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.Design/methodology/approachThe study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O3 index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.FindingsThe proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.Research limitations/implicationsThe study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.Practical implicationsThe findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.Social implicationsWhile primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.Originality/valueThis study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

Sensitivity Analysis of Performance Tests for Six-Degree-of-Freedom Serial Industrial Robots

Abstract The international standard ISO 9283:1998 is popular for performance tests of industrial robots at present. It is desirable that the tests described in this standard should be sensitive to error sources of robot end positioning/orientation. In this paper, first, the kinematic and the joint stiffness model parameters are identified experimentally for two models of six-DOF (degree-of-freedom) serial industrial robots (i.e., the ABB IRB 1410 and UR5 robots). Then, the standard deviations of the derived model parameters are obtained as error inputs for the sensitivity analysis of the performance tests including the positioning/orientation accuracy/repeatability tests. By simulating the error sensitivity of the positioning/orientation accuracy/repeatability test methods for industrial robots, it is analyzed whether the tests described in the ISO 9283:1998 Standard are sensitive to the focused error sources, showing the limitations of the evaluation index of the ISO 9283:1998 Standard. The results show that for six-DOF serial industrial robots, the positioning accuracy test is the key to determining their motion performance. The orientation accuracy and repeatability tests are not necessary if the positioning accuracy and repeatability tests can be done for six-DOF serial industrial robots. Finally, the improvement suggestion of the performance test method is proposed. The research of this paper is beneficial for improving the performance evaluation methods of industrial robots. It can also help robot manufacturing enterprises analyze and improve the positioning/orientation accuracy/repeatability of their products.

Influence of working height on the energy consumption of an industrial robot

Electricity consumption is a big problem not only for the economy, but also for the environment. More and more electricity is used, so it is necessary to find a way to save it. This article focuses on the electrical energy consumption of an industrial robot by changing its working height. The goal is to find out at which height it is most suitable to work in order to achieve the lowest possible energy consumption. Research is realized on industrial robot ABB IRB 6700 using simulation tools such as ABB RobotStudio and Process Simulate, where different trajectory heights are simulated. The trajectories are dimensioned according to the size of the robot. Also, the trajectories are designed to maximize the use of the robot’s axis. This method makes it possible to reduce energy consumption even before the robot is physically located in the factory and its position cannot be changed.

Development of a Digital Twin for the ABB IRB 1200 robot in sheet metal forming processes

This paper aims to detail the steps required to develop a digital twin based on a dynamic model of the ABB IRB 1200 7/0.7 robot (7kg payload, 0.7m maximum reach), using the Simulink platform. This model is needed in order to facilitate its use in robotic forming processes. The developed dynamic model allows a rigorous analysis of the resisting forces influencing the robot structure. ABB IRB 1200, being a small robot with a load capacity of only 7 kg, is ideal for complex simulations in Simulink, which can foresee various positioning configurations of the blank parts relative to the robot base to minimize the impact of the resistive forces.The use of the ABB IRB 1200 robot in roboforming processes is particularly relevant for the MIE department’s robotics lab from the Engineering Faculty of Sibiu, given the availability of two such units that can operate in synchronous mode. The synchronous implementation of two robots in roboforming processes has the potential to significantly improve the accuracy of manufactured components through the use of counter-punching. This method allows for tighter control of the forming process of metal parts, ensuring improved accuracy of finished products.

Kinematic Calibration of an Industrial Manipulator without External Measurement Devices

This paper presents a practical approach to fully automated kinematic calibration of an industrial manipulator. The approach is based on the principle of plane constraint. The electrical signal is used to fix the moment of contact between the conductive tool and the flat surface. The measurement data are manipulator configurations (joint angles) at the moment of contact. A modification of the algorithm to deal with the scaling problem is also proposed. This approach provides both high calibration accuracy and lower cost of the experimental setup compared to coordinate measuring machines (CMMs), laser trackers, and vision systems. The article examines the impact of various methods of kinematic parameterization of manipulators: the Denavit – Hartenberg agreement (DH), product of exponentials (POE), as well as the complete and parametrically continuous model (CPC) on the calibration accuracy. A comparison is made of the open-loop and the proposed closed-loop calibration methods on the Puma 560 model known in the literature. POE parameters were converted to DH and CPC to compare accuracy after calibration based on these parameterizations. The method of computing POE-CPC transformation as a solution to a certain optimization problem is proposed. The problem of identifying geometric parameters in the presence of restrictions is solved by gradient optimization methods. Experiments have been carried out on an ABB IRB 1600 industrial manipulator with an installed conductive probe and an ABB IRBP A-500 robotic positioner with a conductive metal flat surface. A technique for indirectly checking the accuracy of calibration of kinematic parameters is proposed based on a study of the accuracy of manipulation when using these parameters.

Sequential calibration of transmission ratios for joints of 6-DOF serial industrial robots based on laser tracker

PurposeIn traditional calibration methods of kinematics parameters of industrial robots, dozens of model parameters are identified together based on an optimization procedure. Due to different contributions of model parameter errors to the tool center point positioning error of industrial robots, obtaining good results for all model parameters is very difficult. Therefore, the purpose of this paper is to propose a sequential calibration method specifically for transmission ratio parameters, which includes reduction ratios and coupling ratios of industrial robot joints.Design/methodology/approachThe ABB IRB 1410 industrial robot is considered as an example in this study. The transmission ratios for each joint of the robot are identified using the spatial circle fitting method based on spatial vectors, which fit the center and radius of joint rotation with the least squares optimization algorithm. In addition, a method based on the Rodrigues’ formula is designed and presented for identifying the actual coupling ratio of the robot. Subsequently, an experiment is carried out to verify the proposed sequential calibration method of transmission ratios.FindingsIn this experiment, the actual positions of the linkages before and after joint rotations are measured by a laser tracker. Accurate results of the reduction ratios and the coupling ratios are calculated, and the results are verified experimentally. The results show that by calibrating the reduction ratios and coupling ratios of the ABB robot, the rotation angle errors of the robot joints can be reduced.Originality/valueThe authors propose a sequential calibration method for transmission ratio parameters, including reduction ratios and coupling ratios of industrial robot joints. An experiment is carried out to verify this proposed sequential calibration method. This study may be beneficial for calibrating the kinematic parameters of industrial robots and improving their positioning accuracy.

Tri-Projection Neural Network for Redundant Manipulators

Resolving redundancy for manipulators subject to various limits is significant in robotics. The problem is often handled by an optimization formulation with joint velocity being the decision variable, for which how to address the joint acceleration constraint is challenging. Motivated by this problem, a dynamic neural network with triple projections, called tri-projection neural network (TPNN), is developed for quadratic programs with a constraint on the state evolution of the neuron states. The proposed TPNN is applied to resolving redundancy of an ABB IRB 140 industrial manipulator with velocity inputs subject to joint acceleration constraints. Simulation comparisons with an existing method demonstrate the superiority of the developed TPNN in fully employing the acceleration capability of the manipulator.

Uniform vs. Lognormal Kinematics in Robots: Perceptual Preferences for Robotic Movements

Collaborative robots or cobots interact with humans in a common work environment. In cobots, one under-investigated but important issue is related to their movement and how it is perceived by humans. This paper tries to analyze whether humans prefer a robot moving in a human or in a robotic fashion. To this end, the present work lays out what differentiates the movement performed by an industrial robotic arm from that performed by a human one. The main difference lies in the fact that the robotic movement has a trapezoidal speed profile, while for the human arm, the speed profile is bell-shaped and during complex movements, it can be considered as a sum of superimposed bell-shaped movements. Based on the lognormality principle, a procedure was developed for a robotic arm to perform human-like movements. Both speed profiles were implemented in two industrial robots, namely, an ABB IRB 120 and a Universal Robot UR3. Three tests were used to study the subjects’ preference when seeing both movements and another analyzed the same when interacting with the robot by touching its ends with their fingers.

Online position correction approach of an industrial robot by using a new photogrammetric measurement system

Six-axis industrial robots are widely used, mainly because of their versatility. However, they have one drawback: they are not as accurate as conventional machine tools. There are currently a number of approaches to increase the accuracy of these robots. This paper reports on the online correction of an ABB IRB 120 industrial robot using a novel photogrammetric measurement device, the Multi Aperture Positioning System (MAPS). As described by Luhmann, multi-camera solutions were previously required for the online measurement of the six degrees of freedom (6DOF = position and orientation in 3D space) of the robot TCP. MAPS offers the advantages of a single-camera system and at the same time the features and accuracy of a multi-camera system. To increase the accuracy of the robot, a correction algorithm is developed. It uses the measured values from MAPS to calculate the deviation of the robot Tool Center Point (TCP) position from the planned position and corrects it online if necessary. In this paper, three experiments are presented and their results discussed. Two to determine the position and trajectory accuracy of the robot and one to determine its z-stability on a trajectory in the xy plane. In each of these experiments, the robot end effector is tracked with the MAPS measurement device. After the actual state of the robot was recorded, all experiments were performed again, this time with the online correction. In all experiments, a significant increase in the accuracy of the robot can be observed. For example, the mean absolute error (MAE) of the position accuracy improved by a factor of 7 from 0.0207 to 0.0029 mm and the path accuracy (MAE) from 0.1140 to 0.0151 mm.

Kinematics and trajectory planning analysis based on hybrid optimization algorithms for an industrial robotic manipulators

In industrial applications and automation, the robotic manipulators exhibit a significant role. Several complex robotic systems performed a number of industrial works named spray painting, welding, assembly, pick and place action, etc. The end-effector’s position and the joint angles play a vital role since any task is activated inside the pre-defined robotic manipulator’s work space. Also, the problem of trajectory planning is a very challenging task in the robotic fields. To solve these problems, this paper proposes a kinematics analysis and trajectory planning of an Industrial robotic manipulators (IRMs) based on the hybrid optimization algorithms. Here, three IRMs such as PUMA 560 (6 DOF), KUKA LBR iiwa 14 R820 (7DOF) and ABB IRB 140 (6DOF) are considered. For each robot, the forward and the inverse kinematics (IK) are analysed and also the trajectory planning of each robot is discussed using the hybrid optimization algorithms. In this work, 18 optimization algorithms such as PSO (particle swarm optimization), SSO (social spider optimization), DFO (dragonfly optimization), BOA (butterfly optimization), CSA (crow search algorithm), BSA (bird swarm algorithm), SHO (selfish herd optimization), KHO (krill herd optimization), ALO (antlion optimization), ACO (ant colony optimization), GWO (Grey wolf optimization), GOA (grasshopper optimization), SBO (satin bowerbird optimizer), WCO (world cup optimization), COA (cuckoo optimization algorithm), CFA (cuttlefish algorithm), SOA (seagull optimization algorithm), and TSA (tunicate swarm algorithm) are utilized for both forward and inverse kinematic analysis and the trajectory planning problem. For the PUMA 560, KUKA LBR iiwa 14 R820, and ABB IRB 140 IRMs, forward kinematics (FK) are solved by the hybrid combination of PSO-SSO, SHO-KHO, and SBO-WCO individually. Also, the IK are solved by the DFO-BOA, ALO-ACO and COA-CFA for each IRMs. The trajectory planning problem is solved by the CSA-BSA, GWO-GOA and SOA-TSA for each robot individually. These optimization techniques give number of solution for kinematics and trajectory problem but it converses the best solution for the minimum multi-objective function value. Each robot obtained the minimum travelling time for without and with an obstacle which is 0.0118 and 0.0313 s for PUMA and 0.0117 and 0.0310 s for KUKA, and 0.0114 and 0.0120 s for ABB IRB 140 robot. The advantages of these hybrid algorithm are shorter computation time, and fewer iterations. The kinematics and trajectory analysis of each IRM is simulated using robotic tool box in MATLAB with GUI interface. For each IRM, optimized position values of the end effector, joint angles and the best optimal path are computed with minimum objective function. Finally, the performance of each robot is compared to the existing robotic works.

A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines

This manuscript presents a methodology and a practical implementation of a network architecture for industrial robot data acquisition and predictive maintenance . We propose a non-intrusive and scalable robot signal extraction architecture, easily applicable in real manufacturing assembly lines. The novelty of the paper lies in the fact that it is the first proposal of a network architecture which is specially designed to address the predictive maintenance of industrial robots in real production environments. All the infrastructure needed for the implementation of the architecture is comprised of traditional well-known industrial assets. We synchronize the data acquisition with the execution of robot routines using common Programmable Logic Controllers (PLC) to obtain comparable data batches. A network architecture that acquires comparable and structured data over time, is a crucial step to advance towards an effective predictive maintenance of these complex systems, in terms of effectively detecting time dependent degradation. We implement the architecture in a real automotive manufacturing assembly line and show the potential of the solution to detect robot joint failures in real world scenarios. The architecture is therefore specially interesting for industrial practitioners and maintenance personnel. Finally, we test the feasibility of using one-class novelty detection models for robot health status degradation assessment using data of a real robot failure. To the best of our knowledge, this is the first contribution that uses robot torque signals of a real production line failure to train one-class models. • A non-intrusive and scalable robot data acquisition architecture is proposed for real manufacturing assembly lines. • The network acquires synchronized comparable robot operational data over time. • The architecture is able to detect a significant deviation in a faulty ABB IRB 6400r robot data. • One-class novelty detection models detect deviations in real operational conditions.

Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

AbstractIn this paper, an optimal collision‐free trajectory is developed based on the hybrid optimization algorithms for industrial robotic manipulators (IRMs). Three IRMs such as PUMA 560 (six degrees of freedom—6DOF), KUKA LBR iiwa 14 R820 (7DOF), and ABB IRB 140 (6DOF) are considered. The key objective is to enhance the smoothness and efficiency of manufacturing robots by optimum joint trajectory design using the seventh‐order polynomial function. The proposed approach is to solve both kinematics and trajectory planning problems by using the different combinations of the hybrid meta‐heuristic algorithms. The kinematic parameters including jerk, acceleration, and velocity mostly impact the travel smoothness of the robot end‐effector on the trajectory path. Therefore, these parameters are to be constrained for generating the collision‐free path. The endurance of velocity and acceleration can be obtained by reducing the jerk which leads to smooth robotic motion. The proposed work is executed using a robotic toolbox in MATLAB with a graphical user interface. The values of acceleration, velocity, and jerk are computed for the robot joints. Each robot obtained the minimum traveling time for without and with an obstacle which is 0.0118 and 0.0313 s for PUMA and 0.0117 and 0.0310 s for KUKA, and 0.0114 and 0.0120 s for ABB IRB 140 robot. From the experimental outcomes, the proposed scheme of the hybrid optimization algorithms is more effective for the trajectory planning of IRMs than that of other works.

Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation

Robotic systems for research and development of factory automation are complex and unavailable for broad deployment in robotic laboratory settings. The usual robotic factory automation setup consists of series of sensors, robotic arms and mobile robots integrated and orchestrated by a central information system. Cloud-based integration has been gaining traction in recent years. In order to build such a system in a laboratory environment, there are several practical challenges that have to be resolved to come to a point when such a system can become operational. In this paper, we present the development of one such system composed of (i) a cloud-based system built on top of open platform for innovation in logistics, (ii) a prototyped mobile robot with a forklift to manipulate pallets in a “factory” floor, and (iii) industrial robot ABB IRB 140 with a customized gripper and various sensors. A mobile robot is designed as an autonomous four Mecanum wheels system with on-board LiDAR and RGB-D sensor for simultaneous localization and mapping. The paper shows a use case of the overall system and highlights the advantages of having a laboratory setting with real robots for the research of factory automation in a laboratory environment. Moreover, the proposed solution could be scaled and replicated in real factory automation applications.

Image space trajectory tracking of 6-DOF robot manipulator in assisting visual servoing

As vision is a versatile sensor, vision-based control of robot is becoming more important in industrial applications. The control signal generated using the traditional control algorithms leads to undesirable movement of the end-effector during the positioning task. This movement may sometimes cause task failure due to visibility loss. In this paper, a sliding mode controller (SMC) is designed to track 2D image features in an image-based visual servoing task. The feature trajectory tracking helps to keep the image features always in the camera field of view and thereby ensures the shortest trajectory of the end-effector. SMC is the right choice to handle the depth uncertainties associated with translational motion. Stability of the closed-loop system with the proposed controller is proved by the Lyapunov method. Three feature trajectories are generated to test the efficacy of the proposed method. Simulation tests are conducted and the superiority of the proposed method over a Proportional Derivative – Sliding Mode Controller (PD-SMC) in terms of settling time and distance travelled by the end-effector is established in the presence and absence of depth uncertainties. The proposed controller is also tested in real-time by integrating the visual servoing system with a 6-DOF industrial robot manipulator, ABB IRB 1200.

An experimental study of the impact of virtual reality training on manufacturing operators on industrial robotic tasks

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment.

The Performance Parameter Analysis and Calibration Methodology for the Industrial Robot

The article’s primary purpose is to present a new robot calibration system proposed to improve the positional accuracy and repeatability of the ABB IRB 1520ID industrial robot having six degrees of freedom. Various calibration methods of articulated industrial robots are discussed. A new calibration technique is proposed, using a five Linear Variable differential Transformer (LVDT) probe and a master tool designed for varying payload capacity during experimentation. Analysis of different robot parameters and internal or external factors affecting the performance of the robot was done. The planning, execution, and experimentation set up all the stages are mentioned in detail. Finally, after the experimental setup, statistical tools and formulae to evaluate the positional accuracy and repeatability are also described in detail.

SIMULATION MODELING OF MOVEMENT OF INDUSTRIAL ROBOT MANIPULATOR IN THE MATLAB PROGRAM

In the work the information support for carrying out of simulation modeling of the industrial robot manipulator in the Matlab program is developed. The structure of the program for research of kinematics of industrial robot is developed. A graphical interface using the Matlab GUI has been developed for simulation of an industrial robot. The Denova-Hartenberg parameters for modeling the motion of an industrial robot are derived from the 3D model of the ABB IRB 2400 robot manipulator.