A method proposed for training an artificial neural network used for industrial robot programming by demonstration

Stoica, M.; Calangiu, G. A.; Sisak, F.; Sárkány, István

doi:10.1109/optim.2010.5510463

Cited by 8 publications

(7 citation statements)

References 3 publications

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“…A fixed training/testing ratio of 55/45 [13] was taken for testing all the algorithms. When an attempt was made to fit the actual output with the predicted using all the algorithms used, it was noticed that the Bayesian regulation algorithm produced the best results.…”

Section: Training Using Different Methodsmentioning

confidence: 99%

Tracing a Weld Line using Artificial Neural Networks

Rao¹,

Kalaichelvi²,

Karthikeyan³

2018

IJNDC

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KeywordsRobot ANN welding image processing A B S T R A C TRobotic manipulators are becoming increasingly popular nowadays with applications in almost every industry and production line. It is difficult but essential to create a common algorithm for the different types of manipulators present in today's market so that automation can be achieved at a faster rate. This paper aims to present a real-time implementation of a method to control a Tal Brabo! Robotic manipulator to move along a given weld line in order to be utilized in factories for increasing production capacity and decreasing production time. The controller used here is provided by Trio, whose ActiveX component is interfaced to MATLAB. Images were captured to identify weld lines in every possible alignment to find points of interest and the neural network was trained in order to follow a given weld line once the work-piece was placed on the work-table.

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Section: Training Using Different Methodsmentioning

confidence: 99%

Tracing a Weld Line using Artificial Neural Networks

Rao¹,

Kalaichelvi²,

Karthikeyan³

2018

IJNDC

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show abstract

“…In this paper, the target object is considered as the foreground, and it can be extracted pixel-by-pixel if the color difference of each channel between the input image and the background image is too large. The foreground extraction of training data can be expressed as (2) where = R, G, or B, and = 20 is the empirical value. The function _ denotes the binary version of the foreground of the training image, where the value 1 means a foreground object pixel and the value 0 means a background pixel.…”

Section: B Foreground Extractionmentioning

confidence: 99%

“…The function _ denotes the binary version of the foreground of the training image, where the value 1 means a foreground object pixel and the value 0 means a background pixel. Similarly, the binary version of the foreground of the test image, _ , can also be obtained by (2).…”

Section: B Foreground Extractionmentioning

confidence: 99%

“…The meaning of (3) is to find out the shadow pixels, as shown in Fig. 4(b), and remove them from the binary foreground found in (2). In addition, for both training and test data, the interval [(, )] takes into account how strong the light source is and the reflectance issue as well.…”

Section: Shadow Detection and Noise Removingmentioning

confidence: 99%

“…Artificial neural networks are widely used in this type of robot programming. M. Stoica et al developed a system to train an artificial neural network that can be incorporated in industrial robot programming by demonstration [2]. Their work explored how to deal with the This work was supported in part by the National Chung-Shan Institute of Science & Technology under Grants NCSIST-803-V101 and the National Science Council under Grants NSC 102-2218-E-027-016-MY2 and NSC 102-2221-E-027-085.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robot vision to recognize both object and rotation for robot pick-and-place operation

Lin

Chen

2015

2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS)

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This paper presents a robot vision system to recognize both different target objects and their poses that can be incorporated in robot programming by demonstration (robot PbD). In recent years, robot PbD has become a popular topic in the field of robotics. Programming robots is a time-consuming process and requires technical knowledge. However, robot task programming will be simplified by using a vision system to analyze human demonstration. Usually, design of a user-friendly human-robot interaction interface is crucial to implement the robot PbD process. We take pick-and-place operation as an example of robot PbD. Several objects are placed on a table without overlapping, and people pick one of them to other place, one after another. Before learning from human demonstration, if the robot can obtain initial information, e.g., to recognize the target object and to estimate its pose, it helps robot to refine its following decision, such as selecting the gripper type or the grip angle. Thus the authors develop the robot vision ads the first step to realize such working robot. The developed vision system has advantage of interactive trainability with the help of the proposed graphical user interface (GUI), in which the interpretable features can be easily selected by ordinary users. We also propose a simple scale-invariant pose estimation method. Experimental results support feasibility and effectiveness of the system.

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Simulation optimization to microwave components using neural network

Zhou

Nie

Zhang

et al. 2012

Int J Numerical Modelling

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This paper proposes a new technique to train neural network (NN); with the result, we can solve some real-world application problems such as microwave components modeling and optimization. Its major advance is achieved in avoiding the testing error falling into local minimum. After the generalization, the ability of three-layer and four-layer NN is also checked; our investigations show that four-layer NN trained by the proposed training method can map the electromagnetic simulation of microwave components better than its counterpart. Besides, the modeling of microwave circuits and slotted patch antennas is examined to demonstrate the validity of this technique.

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A method proposed for training an artificial neural network used for industrial robot programming by demonstration

Cited by 8 publications

References 3 publications

Tracing a Weld Line using Artificial Neural Networks

Tracing a Weld Line using Artificial Neural Networks

Robot vision to recognize both object and rotation for robot pick-and-place operation

Simulation optimization to microwave components using neural network

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