The avoidance of obstacles placed in the workspace of the robot is aproblem which makes controlling them more difficult. The known avoidance methodsused for the robots control are based on bypass trajectory programming or on usingthe sensors that detect the position of the obstacle. This paper describes a method oftraining 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 anartificial 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 jointcoordinates, as referential values for the controller. The novelty of the method consistsin the deliberately erroneous training of the network, so that, when programming adirect trajectory in the workspace, the robot avoids a known obstacle.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    ANN Method for Control of Robots to Avoid Obstacles


    Beteiligte:
    Ciupan, Emilia (Autor:in) / Lungu, Florin (Autor:in) / Ciupan, Cornel (Autor:in)

    Erscheinungsdatum :

    05.08.2014


    Anmerkungen:

    doi:10.15837/ijccc.2014.5.813
    INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; Vol 9 No 5 (2014): International Journal of Computers Communications & Control (October); 539-554 ; 1841-9844 ; 1841-9836 ; 10.15837/ijccc.2014.5



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Redundant Robot Can Avoid Obstacles

    Homayoun, Seraji / Colbaugh, Richard / Glass, Kristin | NTRS | 1991


    Learning to Avoid Obstacles With Minimal Intervention Control

    Duan A. / Camoriano R. / Ferigo D. et al. | BASE | 2020

    Freier Zugriff


    Planning Motions To Avoid Moving Obstacles

    Fiorini, Paolo / Shiller, Zvi | NTRS | 1995


    Practical Control for Multicopters to Avoid Non-Cooperative Moving Obstacles

    Quan, Quan / Fu, Rao / Cai, Kai-Yuan | ArXiv | 2021

    Freier Zugriff