@article{MAKHILLJEAS2018131016214,
    title = {Manipulation of Tools by Means of a Robotic Arm Using Artificial Intelligence},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {10},
    pages = {3479-3492},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.3479.3492},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.3479.3492},
    author = {M. Paula Catalina,Ruben D. Hernandez and},
    keywords = {Deep convolutional neural network,hand gesture recognition,layer activations,human-robot interaction,tool,real environment},
    abstract = {The following study presents the development of an algorithm of recognition, grip detection and
trajectory planning for a robot of three degrees of freedom where objects are recognized by Convolutional
Neural Networks (CNN) and gripping detection by geometric analysis of the object. The algorithm works on
a non-controlled environment where it receives the images through a webcam, segments all the objects that are
found in them, classifies them into one of three categories of tools (scalpel, scissors, screwdriver) trained on
the CNN and searches for the tool desired by the user on which a feasible gripping point is selected and a path
is executed that allows the manipulator to take the found object and move it to another point. Finally, functional
tests are presented for the trained categories and the results are analyzed to determine grip accuracy in the real
environment.}
    }