Error recognition of robot kinematics parameters based on genetic algorithms

Yan, Ying Ying

doi:10.1007/s12652-020-01781-x

Cited by 15 publications

(7 citation statements)

References 19 publications

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“…With the introduction of observation indices, the optimization problem of the measurement configuration can be transformed into a constrained optimization problem. With the help of the exchange–add–exchange method (Sun and Hollerbach, 2008), tabu search method (Qiao et al , 2021), genetic algorithm (Khalil and Gautier, 1991), simulated annealing method (Yan, 2020) and DETMAX algorithm (Liu and Wang, 2008), the rapid solution of such optimization problems has been realized. When the observation indices are incorporated, an effective optimization system of measurement configuration is achieved.…”

Section: Introductionmentioning

confidence: 99%

Error identification and compensation of 1T2R parallel power head based on trajectory optimization and principal component analysis

Cui

Song

et al. 2023

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Purpose The purpose of this paper is to propose a method for selecting the position and attitude trajectory of error measurement to improve the kinematic calibration efficiency of a one translational and two rotational (1T2R) parallel power head and to improve the error compensation effect by improving the properties of the error identification matrix. Design/methodology/approach First, a general mapping model between the endpoint synthesis error is established and each geometric error source. Second, a model for optimizing the position and attitude trajectory of error measurement based on sensitivity analysis results is proposed, providing a basis for optimizing the error measurement trajectory of the mechanism in the working space. Finally, distance error measurement information and principal component analysis (PCA) ideas are used to construct an error identification matrix. The robustness and compensation effect of the identification algorithm were verified by simulation and through experiments. Findings Through sensitivity analysis, it is found that the distribution of the sensitivity coefficient of each error source in the plane of the workspace can approximately represent its distribution in the workspace, and when the end of the mechanism moves in a circle with a large nutation angle, the comprehensive influence coefficient of each sensitivity is the largest. Residual analysis shows that the robustness of the identification algorithm with the idea of PCA is improved. Through experiments, it is found that the compensation effect is improved. Originality/value A model for optimizing the position and attitude trajectory of error measurement is proposed, which can effectively improve the error measurement efficiency of the 1T2R parallel mechanism. In addition, the PCA idea is introduced. A least-squares PCA error identification algorithm that improves the robustness of the identification algorithm by improving the property of the identification matrix is proposed, and the compensation effect is improved. This method has been verified by experiments on 1T2R parallel mechanism and can be extended to other similar parallel mechanisms.

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Section: Introductionmentioning

confidence: 99%

Error identification and compensation of 1T2R parallel power head based on trajectory optimization and principal component analysis

Cui

Song

et al. 2023

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“…Correction compensation is used to improve the positioning accuracy of an industrial robot to accurately identify the error sources and values. Yan et al 26 . proposed a method to optimize the accuracy of a 6‐degree of freedom parallel robot by genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Correction compensation is used to improve the positioning accuracy of an industrial robot to accurately identify the error sources and values. Yan et al 26 proposed a method to optimize the accuracy of a 6-degree of freedom parallel robot by genetic algorithm. Zhang et al 27 put forward a least square identification algorithm with an improved IGG3 weight function and applied it to the calibration of industrial robot distance error.…”

Section: Introductionmentioning

confidence: 99%

Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling

Shen

Liu

et al. 2023

Quality & Reliability Eng

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To tackle the low identification accuracy of robot positioning, this paper proposes a positioning failure error identification method for industrial robots, combining the Kriging surrogate model and the improved particle swarm optimization algorithm. First, based on the Denavit-Hartenberg and the small displacement screw methods, the kinematic model of a six-degree-of-freedom industrial robot with joint errors is constructed. Then, the Kriging surrogate model of the kinematic error is constructed, which is trained by generated training samples. Finally, the joint error identification of industrial robots is solved by an improved particle swarm optimization algorithm based on exponential inertia weight and sub-population cooperation under the multi-attitude positioning condition. The simulation results show that the proposed optimization algorithm can significantly improve the convergence accuracy and accurately identify the actual errors at each joint of the industrial robot.

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“…It is a method to find the optimal solution or approximate optimal solution to a complex problem by simulating the natural evolution process. It has been used in neural networks 22 , combinatorial optimization 23 , artificial intelligence 24 , 25 , genetic programming 26 , data mining 27 and other fields. The optimization principle is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

Qiu

Sun

et al. 2022

Sci Rep

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Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the epidemic. Based on the SEIR model, the improved SEIR models were established with considering the incubation period, the isolated population, and genetic algorithm (GA) parameter optimization method. The improved SEIR models can predict the trend of the epidemic situation better and obtain the more accurate epidemic-related parameters. Comparing some key parameters, it is capable to evaluate the impact of different epidemic prevention measures and the implementation of different epidemic prevention levels on the COVID-19, which has significant guidance for further epidemic prevention measures.

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Error recognition of robot kinematics parameters based on genetic algorithms

Cited by 15 publications

References 19 publications

Error identification and compensation of 1T2R parallel power head based on trajectory optimization and principal component analysis

Error identification and compensation of 1T2R parallel power head based on trajectory optimization and principal component analysis

Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

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