Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms

Keser, Serkan; Hayber, Şekip Esat

doi:10.1016/j.sna.2021.113071

Cited by 32 publications

(12 citation statements)

References 52 publications

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“…So, let us focus on the third epoch: the training accuracy is 99.75% and the testing accuracy is 99.2%. The obtained numerical results, compared to other similar studies [ 68 ] in which a K-nearest neighbor algorithm and a support vector machine algorithm were used for recognition and classification, in this case, the values of accuracy were 84.2% and 81.6%, respectively. This proves the advantage of neural networks in such problems.…”

Section: Object Recognition In Robot Working Space Using Convolutiona...mentioning

confidence: 79%

“…Ibrahim et al present embedded machine learning methods [ 67 ] for near sensors tactile data processing. Keser et al use ML techniques for surface roughness recognition based on fiber optic tactile sensor data [ 68 ]. In [ 69 ], ML regression algorithms are trained based on proprioceptive sensing for predicting slippage of individual wheels in off-road mobile robots.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

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Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

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Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

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Section: Object Recognition In Robot Working Space Using Convolutiona...mentioning

confidence: 79%

Section: Related Work and Problem Statementmentioning

confidence: 99%

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…There is also experience in combining machine learning and sensors in previous work. For example, fiber optic tactile sensors combined with machine learning algorithms for surface roughness recognition [23].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Liao

Sheng

et al. 2023

IEEE Sensors J.

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“…The use of artificial intelligence has recently increased in many areas of science and engineering, including wireless telecommunications [1,2], optical fibre communications [3][4][5][6][7][8], and optical fibre sensor applications [9][10][11]. In this paper, we explore experimentally the use of the K-Nearest Neighbour algorithm (KNN) for the integration of a fibre optics digital decoder based on light polarization to estimate 32 angular positions of a device.…”

Section: Introductionmentioning

confidence: 99%

Single‐mode optical fibre digital decoder based on polarization using a K‐nearest neighbour algorithm

Ibáñez‐Camarillo,

Martínez‐Piñón,

Márquez‐Olivera

et al. 2023

Electronics Letters

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It is shown experimentally a new digital optical decoding scheme based on the transmission or polarized light at p polarization planes using a K‐Nearest Neighbour (KNN) algorithm through a single‐mode optical fibre at 633 nm. The optical power signal is sent at p polarization planes which constitute p classes required for signal bit recognition. Results show that it is possible to recognize 32 polarizations planes, 5 bits, using 4 features corresponding to the measurement of optical power at 4 different angles at the photodetector side with an average assertiveness of 99.02%.

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Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms

Cited by 32 publications

References 52 publications

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Single‐mode optical fibre digital decoder based on polarization using a K‐nearest neighbour algorithm

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