A Robot Calibration Method Using a Neural Network Based on a Butterfly and Flower Pollination Algorithm

Cao, Hung Quang; Nguyen, Hau Van; Tran, Thuong Ngoc-Cong; Tran, Hoang Ngoc; Jeon, Jae Wook

doi:10.1109/tie.2021.3073312

Cited by 44 publications

(6 citation statements)

References 35 publications

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“…In recent years, the state has put forward the development model of behavior intelligence, and major scientific research institutions attach great importance to behavior analysis. According to the survey data of behavior intelligence in 2020 [ 1 ], behavior recognition and behavior prediction account for 45.6% of the development of intelligent technology, resulting in the increasing demand for behavior intelligence development analysis and penetration into society. China has established research institutions of artificial intelligence, artificial voice, and artificial portrait and applied behavior recognition to video monitoring to provide data for artificial intelligence analysis [ 2 ].Some scholars believe that behavior recognition is the judgment of different behavior points.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition.

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Section: Introductionmentioning

confidence: 99%

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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“…Inspired by the two phenomena of flower biotic and abiotic pollination, Yang et al proposed the FPA in 2010 [22]. Biotic pollination is a global pollination process that is subject to Lévy flight.…”

Section: Flower Pollination Algorithmmentioning

confidence: 99%

“…Flower Pollination Algorithm (FPA) has been proven to succeed in continuous optimization problems, much work has been done effectively in some fields [19,20]. In order to obtain a novel method that could stably solve TSP, many researchers have proposed different versions of the FPA algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Heuristic Routing Optimization Framework for Reducing Traveling Time in Smart Cities

Hai-ying

Xie

Zhu

2023

Preprint

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Increasing of cities number and urban population has created huge impact on smart cities, intelligent tourism and intelligent logistics. We propose a meta-heuristic routing optimization framework (MROF) based on flower pollination algorithm (FPA) to obtain the planned path with the shortest traveling time. Firstly, we design an iteration-based Gaussian selection method to balance the search capability between global exploration and local exploitation. Then, we design two selection strategies based on Lévy flight and Cauchy distribution to select neighboring structures, in which both self-pollination and cross-pollination require suitable updating strategies to improve search ability. Finally, the tabu strategy, to increase the proposed framework’s search ability, is integrated into the proposed method. The experimental results show that the proposed method outperforms the state-of-the-art algorithms significantly.

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“…Many researchers have studied the calibration of industrial robots in the past decades [3,4]. In terms of calibration methods, Song et al proposed an error calibration method with stepwise compensation and verified the correctness of the calibration method by using the UR3 robot as the experimental object [5].…”

Section: Introductionmentioning

confidence: 99%

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

Min¹,

Hu²,

Wen³

et al. 2023

Meas. Sci. Technol.

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In this paper, a new kinematic parameter error calibration method based on the Levenberg–Marquard and the beetle antennae search algorithm is proposed to enhance the positioning accuracy of the industrial robot. Firstly, the Modified Denavit–Hartenberg model is chosen to establish the kinematic model for the industrial robot. Secondly, the kinematic parameter errors are calibrated by Levenberg–Marquard algorithm and then obtain the kinematic parameter errors of the industrial robot. Thirdly, these kinematic parameter errors are taken as the center values for the initial individual of the beetle antennae search algorithm. The kinematic parameter errors are accurately calibrated using the beetle antennae search algorithm to obtain the best value for the kinematic parameter errors. Finally, experimental verification results demonstrate that the positioning error of the industrial robot is decreased from 0.7332 mm to 0.1392 mm by using the proposed Levenberg–Marquard and beetle antennae search algorithm. The results also demonstrate that the proposed Levenberg–Marquard and beetle antennae search algorithm calibrate the kinematic parameter errors of the industrial robot effectively and enhance the positioning accuracy of the industrial robot significantly.

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A Robot Calibration Method Using a Neural Network Based on a Butterfly and Flower Pollination Algorithm

Cited by 44 publications

References 35 publications

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

A Meta-Heuristic Routing Optimization Framework for Reducing Traveling Time in Smart Cities

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

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