Percorrer por autor "Kazheunikau, M."
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- Configuration space synthesis for robotic manipulators using neural networksPublication . Pashkevich, A.; Ruano, Antonio; Kazheunikau, M.The paper deals with configuration space syntheses for industrial robotic manipulators. A new efficient method is proposed that is based on a neural network collision model. To generate the collision model, a modification of the Radial Basis Function Network (RBFN) is used, which is trained applying the developed algorithm. An obstacle transformation algorithm that is based on conjugate vector model of a robotic cell is proposed. The method has been successfully applied to the design of a robotic manufacturing cell for the automotive industry.
- Identification-based condition monitoring using neural network approachPublication . Pashkevich, A.; Ruano, Antonio; Kulikov, G. G.; Kazheunikau, M.The paper focuses on enhancement of condition monitoring techniques in application to hydro- and electromechanical servomechanisms, which are widely used both in industrial robots and aircraft equipment. Its particular contribution lies in the area of neural network application for identification data analysis, which allows early diagnosis of process faults, while the plant is still operating in a controllable region. The proposed technique has been implemented in a software tool that allows to automate the decision-making process and to visualize the analysis results.
- Neural network approach to collision free path planning for robotics manipulatorsPublication . Ruano, Antonio; Pashkevich, A.; Kazheunikau, M.Abstract: The paper deals with collision free path planning for industrial robotic manipulators. A new efficient algorithm is proposed that is based on a topologically ordered neural network model. This model describes harmonic potential field of the robot configuration space, sampled by non-regular grid. The algorithm has been successfully applied to the off-line programming of a robotic manufacturing cell for the automotive industry.
- Neural network approach to collision free path-planning for robotic manipulatorsPublication . Pashkevich, A.; Kazheunikau, M.; Ruano, AntonioThe paper deals with collision free path-planning for industrial robotic manipulators A new efficient approach is proposed that is based on the topologically ordered neural network model. This model describes harmonic potential field of the robot configuration space, sampled by the non-regular grid. The developed path-planning algorithm takes into account highly-irregular shape of the obstacles of welding and assembling robotic cells, and provides reduced number of collision checking. The stability of the topologically ordered neural network is investigated. The algorithm has been successfully applied to the off-line programming of a robotic manufacturing cell for the automotive industry.
- Neuro-fuzzy modelling of a plant growth in a hydroponic greenhousePublication . Kazheunikau, M.; Ferreira, P. M.; Ruano, AntonioThis paper deals with the modeling of dry matter production in a hydroponic greenhouse. Identification techniques are applied for the modeling, based on fuzzy logic and B-spline neural networks, for two growth models. For the design of these models subtractive clustering, the ASMOD algorithm and genetic programming are employed and compared. The developed approach has been successfully applied for the prediction of tomato growth.