The influence of the load on the behavior of the electromechanical variator

Ciupan, Cornel; Steopan, Mihai; Ciupan, Emilia

doi:10.1109/aqtr.2012.6237715

Cited by 1 publication

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further research illustrated the difficulty of obtaining an effective neural model that can lead the effector with a precision that is appropriate to the application, and avoid the obstacle. There have been circumstances in which the neural model did not offer the coordinates that lead the effector beside the obstacle [3]. The validation of the models presented [3], [4] has been made based on sets of data that have not been used as training data; there has been completed no cross-validation.…”

Section: Introductionmentioning

confidence: 99%

“…In [4] there has been presented the main principle of a method of obstacle avoidance, by means of an erroneous instruction of the network. The experiments have been completed in a smaller part of the robots workspace (a cube with the side 10 cm).…”

Section: Introductionmentioning

confidence: 99%

“…Also, [3] shows a neural model for the kinematical analysis of six parallel robot. For reasons related to simplification, there has been considered the move of the effector in a cube with a side of 10 cm, without taking into account the variation of the position angles.…”

Section: Introductionmentioning

confidence: 99%

“…The program presented in [1], [2] was designed with the purpose of using neural computing in the modelling and the simulation of processes or activities. It is suitable for the study of any activity for which a three-layer perceptron neural network may serve as a model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ANN Method for Control of Robots to Avoid Obstacles

Ciupan

Lungu

Ciupan

2014

INT J COMPUT COMMUN

Self Cite

View full text Add to dashboard Cite

The avoidance of obstacles placed in the workspace of the robot is a problem which makes controlling them more difficult. The known avoidance methods used for the robots control are based on bypass trajectory programming or on using the sensors that detect the position of the obstacle. This paper describes a method of training industrial robots in order for them to avoid certain obstacles in the workspace. The method is based on the modelling of the robot's kinematics by means of an artificial neural network and by including the neural model in the robot's controller. The neural model simulates the robot's inverse kinematics, and provides the joint coordinates, as referential values for the controller. The novelty of the method consists in the deliberately erroneous training of the network, so that, when programming a direct trajectory in the workspace, the robot avoids a known obstacle.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations