Development of a calibrating algorithm for Delta Robot’s visual positioning based on artificial neural network

Ding, Wei; Gu, Jinan; Tang, Shuang; Shang, Zhenyang; Duodu, Enock A.; Zheng, Changjun

doi:10.1016/j.ijleo.2016.06.126

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…Different neural networks such as radial basis function and back-propagation networks have been widely used to compensate the absolute positioning error [19][20][21][22]. The compensation results depend on the network structure and parameters including thresholds, initial weights, and number of hidden neurons.…”

Section: The Proposed Neural Networkmentioning

confidence: 99%

“…where g is the number of iterations, ε g gbest is the neural network parameter providing the highest performance, and ε Test individuals ε T are then generated through crossover using Equation (20), CR is a real-valued crossover probability factor in range [0,1] that controls the probability that a trial vector parameter will be randomly chosen. Generally, CR affects the convergence velocity and robustness of the search process.…”

Section: Differential Evolution Optimizationmentioning

confidence: 99%

“…Neural networks allow one to compensate positioning errors, avoid complex modeling, and comprehensively consider the influence of every error source. Ding et al proposed a neural network combining with Faugeras vision system calibration technology for accurate calibration of a delta-robot vision system [20]. Likewise, Jang et al used a radial basis function network to approximate the relationship between the robot joint readings and corresponding position errors [21].…”

Section: Introductionmentioning

confidence: 99%

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Absolute Positioning Accuracy Improvement in an Industrial Robot

Jiang

Jia

et al. 2020

Sensors

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The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

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Section: The Proposed Neural Networkmentioning

confidence: 99%

Section: Differential Evolution Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Absolute Positioning Accuracy Improvement in an Industrial Robot

Jiang

Jia

et al. 2020

Sensors

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“…Monocular vision positioning uses a single camera to collect information about the target and calculate its motion parameters, offering the advantages of simple computing, low cost, and ease of installation. However, the limitations of the monocular acquisition window result in its inability to effectively accomplish localisation in complex environments, and monocular systems do not have access to height information about the target environment, so they cannot perform the task of reconstructing three-dimensional space [9,10]. Binocular stereo vision can simultaneously obtain two images about the target and compute and process them, and through triangulation can obtain three-dimensional information about the surrounding environment to reconstruct three-dimensional space [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Methods on Visual Positioning Based on Basketball Shooting Direction Standardisation

Yi-qun

Xiao-yun

2022

Scientific Programming

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The existing basketball training shooting direction correction method has the problems of low correction accuracy and poor self-adaptability, and proposes a basketball training shooting direction correction method based on visual perception. A visual localisation algorithm for tracking feature points of object targets is used as the basis for the process of visual robot localisation and its effects, from camera calibration, template matching, background modelling and foreground target separation to feature point extraction, motion estimation, and Kalman filtering. An in-depth understanding and analysis of the traditional corner point detection algorithm is presented, on the basis of which improvements are proposed. An accurate tracking method based on improved Harris corner point extraction is introduced, which builds on the traditional Harris feature point detection by using the changing relationship between the gradient of the grey value of the pixels near the corner point, using simple operations and analysis to exclude some pseudocorner points and noncorner points, and further processing the retained points to derive the correct feature points. The code of this algorithm is written to finally achieve its detection effect, and compared with the traditional algorithm, it is concluded that this algorithm can then extract more accurate corner points in a shorter time, which lays the foundation for the next step of accurate basketball tracking, reflecting the practicality of this algorithm.

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“…Classical studies on indoor object recognition mainly relied on machine learning techniques (Ding et al, 2016(Ding et al, , 2017Mei, Yang, & Yin, 2017;Nan, Xie, & Sharf, 2012;Serre, Wolf, Bileschi, Riesenhuber, & Poggio, 2007;Uijlings, van de Sande, Gevers, & Smeulders, 2013). However, these methods involve a complex pipeline design and cannot learn deep features to generalize their extension.…”

Section: Introductionmentioning

confidence: 99%

Prior knowledge-based deep learning method for indoor object recognition and application

Ding

Luo

et al. 2018

Systems Science & Control Engineering

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Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision. ARTICLE HISTORY

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Development of a calibrating algorithm for Delta Robot’s visual positioning based on artificial neural network

Cited by 12 publications

References 17 publications

Absolute Positioning Accuracy Improvement in an Industrial Robot

Absolute Positioning Accuracy Improvement in an Industrial Robot

Methods on Visual Positioning Based on Basketball Shooting Direction Standardisation

Prior knowledge-based deep learning method for indoor object recognition and application

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