Mihalca, Vlad OvidiuBirouaș, FlaviuAvram, FlorinNilgesz, Arnold2020-09-112020-09-112018-04-20Recent Innovations in Mechatronics, Vol. 5 No. 1 (2018) , 1-5.https://hdl.handle.net/2437/295743Deep Learning usage is spread across many fields of application. This paper presents details from a selected variety of works published in recent years to illustrate the versatility of the Deep Learning techniques, their potential in current and future research and industry applications as well as their state-of-the-art status in vision tasks, where their efficiency is experimentally proven to near 100% accuracy. The presented applications range from navigation to localization, object recognition and more advanced interactions such as grasping.Deep Learning usage is spread across many fields of application. This paper presents details from a selected variety of works published in recent years to illustrate the versatility of the Deep Learning techniques, their potential in current and future research and industry applications as well as their state-of-the-art status in vision tasks, where their efficiency is experimentally proven to near 100% accuracy. The presented applications range from navigation to localization, object recognition and more advanced interactions such as grasping.application/pdfdeep learningneural networkRNNCNNmobile robotsdeep learningneural networkRNNCNNmobile robotsfolyóiratcikkOpen AccessVlad Ovidiu Mihalca, Flaviu Birouaș, Florin Avram, Arnold Nilgeszhttps://doi.org/10.17667/riim.2018.1/8Recent Innovations in Mechatronics152064-9622