A novel calibration method for kinematic parameter errors of industrial robot based on Levenberg–Marquard and Beetle Antennae Search algorithm

Min, Fan; Hu, Zhao; Wen, Jia; Yu, Liandong; Xia, Haojie

doi:10.1088/1361-6501/ace6c5

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The training data and the iteration period are also guaranteed to be constant. The Particle Swarm Optimization algorithm (PSO) [40], Beetle Antennae Search algorithm (BAS) [41], Sparrow Search algorithm (SSA) [42], and MVO-optimized SVM are used with the same image feature parameters and time delay compensation, respectively, and the prediction effect is finally observed using the same set of test data. Since optimization algorithms are generally strongly stochastic, ten sets of tests are performed for each algorithm, and the Wilcoxon sign rank test [43] is performed between the RMSEs of the prediction results of the three comparison algorithms and the RMSEs of the MVO, respectively, to prove the optimization effectiveness of the MVO algorithm.…”

Section: Model Test Resultsmentioning

confidence: 99%

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Zhao,

Zhou,

et al. 2023

Sustainability

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In order to accurately predict the key operating parameters of waste incinerators, this paper proposes a prediction method based on big data and a Multi-Verse Optimizer deep learning model, thus providing a powerful reference for controlling the optimization of the incinerator combustion process. The key operating parameters that were predicted, according to the control objectives, were determined to be the steam flow, gas oxygen, and flue temperature. Firstly, a large amount of measurement data were collected, and 27 relevant control system parameters with a high correlation with the predicted variables were obtained via a mechanism analysis. The input variables of the prediction model were further determined using the improved WesselN symbolic transfer entropy algorithm. The delay time between the variables was found using a gray correlation coefficient, the prediction time was determined to be 6 min according to the delay time distribution of the flame feature, and the time delay compensation was applied to each parameter. Finally, the support vector machine was optimized using a Multi-Verse Optimization algorithm to complete the prediction of the key operating parameters. Experiments showed that the root mean square error of the proposed model for the three output variables—the steam flow, gas oxygen, and flue temperature—were 0.3035, 0.2477, and 1.6773, respectively, which provides a high accuracy compared to other models.

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Section: Model Test Resultsmentioning

confidence: 99%

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Zhao,

Zhou,

et al. 2023

Sustainability

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“…Experiments on diverse datasets validate its efficacy. More studies on the improved BAS algorithm can be found in the literature (Fan et al 2023.…”

Section: Othersmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.

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Industrial robot calibration optimization method based on R-optimal criterion and improved IOOPS algorithm

Jia,

Zeng,

Zhang

et al. 2024

Meas. Sci. Technol.

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In this paper, a novel kinematic parameter calibration method based on the R-optimal criterion and the improved IOOPS (iterative one-by-one pose search) algorithm is proposed to improve the end-effector positioning accuracy of industrial robots. First, a novel industrial robot error calibration model based on the Product of Exponential (POE) model and generalized error model is established, and the ideal pose transformation matrix from the robot base coordinate system to the laser tracker measurement coordinate system is obtained using the least-squares method, thereby obtaining the initial parameter vector of the robot calibration system. Second, various joint angle values at different positions within the robot's workspace and the corresponding end-effector coordinate data, namely calibration sampling point data, are collected. Subsequently, the improved IOOPS algorithm combined with the observability index O_R proposed by the R-optimal criterion is used to select the optimized calibration sampling points. Finally, the optimized sampling point data are combined with the Levenberg-Marquardt (LM) algorithm to optimize the parameters and obtain the optimal kinematic parameters. The experimental results show that the average end-effector error of the robot decreases from 0.5822 to 0.2492 mm after calibration using this method, demonstrating the effectiveness of the optimized sampling points in the calibration process achieved through the improved algorithm and the new observability index.

show abstract

A novel calibration method for kinematic parameter errors of industrial robot based on Levenberg–Marquard and Beetle Antennae Search algorithm

Cited by 3 publications

References 28 publications

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Industrial robot calibration optimization method based on R-optimal criterion and improved IOOPS algorithm

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