Industrial Robot Kinematics Parameter Identification

2014 ◽  
Vol 889-890 ◽  
pp. 1136-1143
Author(s):  
Yong Gui Zhang ◽  
Chen Rong Liu ◽  
Peng Liu
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Parameter Identification ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Robot Kinematics ◽  
Kinematic Parameters ◽  
Unknown Parameters ◽  
Preliminary Measurement ◽  
Kinematics Modeling

For an industrial robots with unknown parameters, on the basis of preliminary measurement and data of the Cartesian and joints coordinates which are shown on the FlexPendant, the kinematic parameters is identified by using genetic algorithms and accurate kinematics modeling of the robot is established. Experimental data could prove the validity of this method.

scholarly journals Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Guanbin Gao ◽  
Fei Liu ◽  
Hongjun San ◽  
Xing Wu ◽  
Wen Wang
Keyword(s):  
Parameter Identification ◽  
Bp Neural Network ◽  
Hybrid Algorithm ◽  
Industrial Robot ◽  
Pso Algorithm ◽  
Kinematic Parameter ◽  
Industrial Robots ◽  
Identification Algorithm ◽  
Kinematic Parameters ◽  
Kinematic Parameter Identification

A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit-Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter-identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm.

scholarly journals Dynamic Model Identification for 6-DOF Industrial Robots

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao ◽  
Yuxuan Yang
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Model Identification ◽  
Friction Model ◽  
Industrial Robots ◽  
Parameters Identification ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Joint Friction ◽  
Dynamical Parameters

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

The Trajectory Planning and Simulation for Industrial Robot Based on Fifth-Order B-Splines

2014 ◽  
Vol 538 ◽  
pp. 367-370 ◽  
Author(s):  
Zhi Jian Gou ◽  
Cheng Wang
Keyword(s):  
Trajectory Planning ◽  
Industrial Robot ◽  
Joint Space ◽  
Robot Kinematics ◽  
Kinematic Parameters ◽  
B Spline ◽  
B Splines ◽  
Robot Trajectory ◽  
Fifth Order ◽  
Parameter Constraints

The trajectory is planned with fifth-order uniform B-splines for the industrial robot aimed to assure the motion is smooth and the trajectory is fourth-order continuous. Under the premise to satisfy the initial kinematic parameters of the robot as zero, its speed, acceleration and jerk are continuous. Based on B-spline theory, process five B-spline curve function is calculated inversely in joint space. Under the robot kinematics parameter constraints, using fifth-order B-spline interpolates to plan robot trajectory when known interpolation points and the kinematic parameters are simulated and validated by the software of ADAMS.So it provides an effective new method for the trajectory planning.

Modeling of Hydraulic Pipeline Transients Accompanied With Cavitation and Gas Bubbles Using Parallel Genetic Algorithms

2008 ◽  
Vol 75 (4) ◽  
Author(s):  
Songjing Li ◽  
Chifu Yang ◽  
Dan Jiang
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Mathematical Models ◽  
Gas Bubbles ◽  
Computation Time ◽  
Unknown Parameters ◽  
Pressure Transients ◽  
Parallel Genetic Algorithms ◽  
Simulation Results ◽  
Transient Models

Mathematical models of pressure transients accompanied with cavitation and gas bubbles are studied in this paper to describe the flow behavior in a hydraulic pipeline. The reasonable prediction for pressure transients in a low pressure hydraulic pipeline largely depends on several unknown parameters involved in the mathematical models, including the initial gas bubble volumes in hydraulic oils, gas releasing and resolving time constants. In order to identify the parameters in the mathematical models and to shorten the computation time of the identification, a new method—parallel genetic algorithm (PGA)—is applied in this paper. Based on the least-square errors between the experimental data and simulation results, the fitness function of parallel genetic algorithms is programed and implemented. The global optimal parameters for hydraulic pipeline pressure transient models are obtained. The computation time of parallel genetic algorithms is much shorter than that of serial genetic algorithms. By using PGAs, the executing time is 20h. However, it takes about 204h by using GAs. Simulation results with identified parameters obtained by parallel genetic algorithms agree well with the experimental data. The comparison between simulation results and the experimental data indicates that parallel genetic algorithms are feasible and efficient to estimate the unknown parameters in hydraulic pipeline transient models accompanied with cavitation and gas bubbles.

scholarly journals Dynamic Simulation Modeling of Industrial Robot Kinematics in Industry 4.0

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Guang Jin ◽  
Shuai Ma ◽  
Zhenghui Li
Keyword(s):  
Dynamic Simulation ◽  
Trajectory Planning ◽  
Simulation Modeling ◽  
Industry 4.0 ◽  
Industrial Robot ◽  
Error Model ◽  
Position Error ◽  
Industrial Robots ◽  
Robot Kinematics ◽  
Kinetic Characteristics

This paper studies the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment and guides the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment in the context of the research. To address the problem that each parameter error has different degrees of influence on the end position error, a method is proposed to calculate the influence weight of each parameter error on the end position error based on the MD-H error model. The error model is established based on the MD-H method and the principle of differential transformation, and then the function of uniform variation of six joint angles with time t is constructed to ensure that each linkage geometric parameter is involved in the motion causing error accumulation. Through the analysis of the robot marking process, the inverse solution is optimized for multiple solutions, and a unique engineering solution is obtained. Linear interpolation, parabolic interpolation, polynomial interpolation, and spline curve interpolation are performed on the results after multisolution optimization in the joint angle, and the pros and cons of various interpolation results are analyzed. The trajectory planning and simulation of industrial robots in the Industry 4.0 environment are carried out by using a special toolbox. The advantages and disadvantages of the two planning methods are compared, and the joint space trajectory planning method is selected to study the planning of its third and fifth polynomials. The kinetic characteristics of the robot were simulated and tested by experimental methods, and the reliability of the simulation results of the kinetic characteristics was verified. The kinematic solutions of industrial robots and the results of multisolution optimization are simulated. The methods, theories, and strategies studied in this paper are slightly modified to provide theoretical and practical support for another dynamic simulation modeling of industrial robot kinematics with various geometries.

scholarly journals Vibration Suppression for Improving the Estimation of Kinematic Parameters on Industrial Robots

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
David Alejandro Elvira-Ortiz ◽  
Rene de Jesus Romero-Troncoso ◽  
Arturo Yosimar Jaen-Cuellar ◽  
Luis Morales-Velazquez ◽  
Roque Alfredo Osornio-Rios
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Vibration Suppression ◽  
Industrial Robot ◽  
Angular Position ◽  
Industrial Robots ◽  
Industrial System ◽  
Kinematic Parameters ◽  
Fusion Technique ◽  
Motion Profile

Vibration is a phenomenon that is present on every industrial system such as CNC machines and industrial robots. Moreover, sensors used to estimate angular position of a joint in an industrial robot are severely affected by vibrations and lead to wrong estimations. This paper proposes a methodology for improving the estimation of kinematic parameters on industrial robots through a proper suppression of the vibration components present on signals acquired from two primary sensors: accelerometer and gyroscope. A Kalman filter is responsible for the filtering of spurious vibration. Additionally, a sensor fusion technique is used to merge information from both sensors and improve the results obtained using each sensor separately. The methodology is implemented in a proprietary hardware signal processor and tested in an ABB IRB 140 industrial robot, first by analyzing the motion profile of only one joint and then by estimating the path tracking of two welding tasks: one rectangular and another one circular. Results from this work prove that the sensor fusion technique accompanied by proper suppression of vibrations delivers better estimation than other proposed techniques.

scholarly journals Probabilistic information fusion to model the pose-dependent dynamics of milling robots

2020 ◽  
Vol 14 (4) ◽  
pp. 435-444
Author(s):  
Maximilian Busch ◽  
Florian Schnoes ◽  
Thomas Semm ◽  
Michael F. Zaeh ◽  
Birgit Obst ◽  
...  
Keyword(s):  
Experimental Data ◽  
Information Fusion ◽  
Dynamic Properties ◽  
Industrial Robot ◽  
Milling Process ◽  
Industrial Robots ◽  
Robot Dynamics ◽  
Data Driven Approach ◽  
Conventional Machine ◽  
Fusion Scheme

Abstract Conventional industrial robots are increasingly used for milling applications of large workpieces due to their workspace and their low investment costs in comparison to conventional machine tools. However, static deflections and dynamic instabilities during the milling process limit the efficiency and productivity of such robot-based milling systems. Since the pose-dependent dynamic properties of the industrial robot structures are notoriously difficult to model analytically, machine learning methods are recently gaining more and more popularity to derive system models from experimental data. In this publication, a modeling concept based on a modern information fusion scheme, fusing simulation and experimental data, is proposed. This approach provides a precise model of the robot’s pose-dependent structural dynamics and is validated for a one-dimensional variation of the robot pose. The results of two information fusion algorithms are compared with a conventional, data-driven approach and indicate a superior model accuracy regarding interpolation and extrapolation of the pose-dependent dynamics. The proposed approach enables decreasing the necessary amount of experimental data needed to assess the vibrational properties of the robot for a desired pose. Additionally, the concept is able to predict the robot dynamics at poses where experimental data is very costly to gather.

The Theory of Kinematic Parameter Identification for Industrial Robots

1988 ◽  
Vol 110 (1) ◽  
pp. 96-100 ◽  
Author(s):  
L. J. Everett ◽  
Tsing-Wong Hsu
Keyword(s):  
Parameter Identification ◽  
Recent Literature ◽  
Robot Manipulators ◽  
A Priori ◽  
Kinematic Parameter ◽  
Industrial Robots ◽  
Identification Algorithm ◽  
Kinematic Parameters ◽  
Independent Parameters ◽  
Kinematic Parameter Identification

This paper presents the concept of completeness for kinematic identification of robot manipulators. Completeness is defined as the ability to map joint positions into tool positions for all arbitrary manipulators. It is suggested that complete models must contain a certain number of independent parameters. Furthermore it is suggested (and shown by practical examples) that the required number of independent kinematic parameters is easy to determine a-priori. This enables one to check a model for completeness. Although the basic idea behind kinematic identification may have been considered well known, several identification algorithms in the recent literature are incomplete. Two examples are included in this paper. For this reason, this paper presents the topic and includes the conditions for a complete and viable identification algorithm.

scholarly journals Kinematic Calibration of Industrial Robots Based on Distance Information Using a Hybrid Identification Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guanbin Gao ◽  
Yuan Li ◽  
Fei Liu ◽  
Shichang Han
Keyword(s):  
Industrial Robot ◽  
Kinematic Model ◽  
Error Model ◽  
Industrial Robots ◽  
Kinematic Calibration ◽  
Kinematic Parameters ◽  
Distance Information ◽  
End Effector ◽  
Hybrid Identification ◽  
Identification Method

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is linearized for the convenience of the following identification. The singular value decomposition (SVD) is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter (EKF) and regularized particle filter (RPF) hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved.

scholarly journals Dynamic Model Parameter Identification and Simulation of SCR Based on Genetic Algorithm§

2015 ◽  
Vol 9 (1) ◽  
pp. 62-66
Author(s):  
Ren Hongjuan ◽  
Lou Diming ◽  
Zhu Jian ◽  
Luo Yiping
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Kinetic Model ◽  
Parameter Identification ◽  
Dynamic Model ◽  
Unknown Parameters ◽  
Exhaust Temperature ◽  
Allowable Error ◽  
Simulation Results ◽  
Model Equations

The Selective Catalytic Reduce (SCR) is studied. The unknown parameters of the SCR kinetic model equations are fitted based on the Genetic Algorithm (GA), which is in the range of the allowable error, compared to the experimental data. Then in AVL Boost software, the simulation results of SCR reaction are obtained. Compared to the test data, the simulation results prove that the parameter identification is effective. At last, the SCR reaction is simulated in AVL Boost, and at the same exhaust temperature, the effect of GHSV and NSR on the SCR reaction is studied.

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