Robotics and Prosthetics at Cleveland State University: Modern Information, Communication, and Modeling Technologies

Kondratenko, Yuriy; Khademi, Gholamreza; Azimi, Vahid; Ebeigbe, Donald; Abdelhady, Mohamed; Fakoorian, Seyed Abolfazl; Barto, Taylor; Roshanineshat, Arash; Atamanyuk, Igor; Simon, Dan

doi:10.1007/978-3-319-69965-3_8

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…The simulation results confirm the universality and efficiency of the proposed computational library of the horizontal and vertical analytic models for diverse practical applications. The computation library application can be recommended for fuzzy data processing in solving different control and decision-making problems, for example, for choosing the optimal model of the "university-industry" cooperation [48], selection of partners in business, education, sport or culture exchange [49][50][51], route planning and optimization in uncertainty [52][53][54], portfolio selection [40], evaluation of the qualification level of the specialists, control of robots in dynamic environment [55,56], control of industrial processes [13,57] with multi-sensor data processing, and others. Application of the developed computational library (29)-(52) is limited to the usage of the triangular form of FNs.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Fuzzy Data Processing Based on a Computational Library of Analytic Models

Kondratenko

2018

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This work focuses on fuzzy data processing in control and decision-making systems based on the transformation of real-timeseries and high-frequency data to fuzzy sets with further implementation of diverse fuzzy arithmetic operations. Special attention was paid to the synthesis of the computational library of horizontal and vertical analytic models for fuzzy sets as the results of fuzzy arithmetic operations. The usage of the developed computational library allows increasing the operating speed and accuracy of fuzzy data processing in real time. A computational library was formed for computing of such fuzzy arithmetic operations as fuzzy-maximum. Fuzzy sets as components of fuzzy data processing were chosen as triangular fuzzy numbers. The analytic models were developed based on the analysis of the intersection points between left and right branches of considered triangular fuzzy numbers with different relations between their parameters. Our study introduces the mask for the evaluation of the relations between corresponding parameters of fuzzy numbers that allows to determine the appropriate model from the computational library in automatic mode. The simulation results confirm the efficiency of the proposed computational library for different applications.

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Section: Discussionmentioning

confidence: 99%

Real-Time Fuzzy Data Processing Based on a Computational Library of Analytic Models

Kondratenko

2018

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“…Intellectual properties are essential for modern robots to gain experience and adapt to natural nonstationary working environments for executing various missions. Service robots acting in uncertain conditions have become even more widely used in recent years [ 7 , 8 , 9 , 10 ]. Many modern robots act in clinics, offices, supermarkets, cinemas, enterprises, etc.…”

Section: Introductionmentioning

confidence: 99%

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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“…Let us note also that computer modeling methods for the implementation of implants and robotics at the research projects have a lot of influence on scientific investigation and learning [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Robotic Plasma Spraying System for Implants of Complex Structure: 3d Model and Motion Planning

Karymsakova¹,

Denissova²,

Kumargazhanova³

et al. 2020

IJC

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The problem of a robotic system creating for plasma spraying of biocompatible coatings on complex shaped implants based on a Fanuc LR Mate 200 id manipulation robot and modeling spraying trajectories using a virtual simulator Roboguide V6.40 is considered. Parametric classification of implants is carried out whenever possible by plasma spraying using robotic devices. The main procedures for implants preparation for spraying were investigated. The 3D UNIVERSE scanner is used for scanning the implant and building its spatial model, the 3D model of the implant is being developed in Geomagic Design X. To build the manipulator program movements taking into account the speed of movement, it is proposed to use fourth order splines, which is built in the Matlab tools with finding of the optimal close spline to the original values. As the example of Cox femoral joint implant, a simulation of robot motion is performed using a virtual simulator Roboguide V6.40 with the possibility of transferring the program to a real Fanuc LR Mate 200 id robot.

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Robotics and Prosthetics at Cleveland State University: Modern Information, Communication, and Modeling Technologies

Cited by 11 publications

References 35 publications

Real-Time Fuzzy Data Processing Based on a Computational Library of Analytic Models

Real-Time Fuzzy Data Processing Based on a Computational Library of Analytic Models

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

Robotic Plasma Spraying System for Implants of Complex Structure: 3d Model and Motion Planning

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