Training an Under-actuated Gripper for Grasping Shallow Objects Using Reinforcement Learning

التفاصيل البيبلوغرافية
العنوان: Training an Under-actuated Gripper for Grasping Shallow Objects Using Reinforcement Learning
المؤلفون: Mohammed, Wael M., Nejman, Mirosław, Castaño, Fernando, Lastra, Jose L. Martinez, Strzelczak, Stanisław, Villalonga, Alberto
المساهمون: Tampere University, Automation Technology and Mechanical Engineering
بيانات النشر: IEEE
سنة النشر: 2020
مصطلحات موضوعية: 213 Electronic, automation and communications engineering, electronics
الوصف: Robot programming and training depends on the task that needs to be completed, the end-effector properties and functionalities and the working space. These considerations can complicate the programming process, which in return, increase the time that is needed for training the robot. Thus, several research approaches have been introduced to address training the robots intuitively. In this regard, this paper presents an approach for training an under-actuated gripper and the robot attached to it for grasping shallow objects. The research work started by detailed analysis of the fingers of human hand during the grasping process. Then, a modified design of the gripper has been produced. This modification includes adding an artificial nail among other hardware-related modifications. Then, a Q-Learning algorithm has been used for training the gripper on grasping the shallow object. With two fingers, three actions were configured, and 625 states were configured for the learning algorithm. For the validation, a coin has been used for representing the shallow object. The results showed reduction in both the grasping time and the number of movements. ; acceptedVersion ; Peer reviewed
نوع الوثيقة: conference object
وصف الملف: fulltext
اللغة: English
ردمك: 978-1-72816-390-1
978-1-72816-389-5
1-72816-390-0
1-72816-389-7
Relation: PURE: 43033901; PURE UUID: 67069e60-1889-49d5-bb41-f9cc815b8beb; RIS: urn:49F6B8CF413E1964D666D162ACE8B97F; Scopus: 85098706179; ORCID: /0000-0001-6227-3408/work/99255365; ORCID: /0000-0002-8364-3348/work/99265170; https://trepo.tuni.fi/handle/10024/130115; URN:NBN:fi:tuni-202012098636
DOI: 10.1109/ICPS48405.2020.9274727
الاتاحة: https://trepo.tuni.fi/handle/10024/130115
https://doi.org/10.1109/ICPS48405.2020.9274727
Rights: openAccess
رقم الانضمام: edsbas.EDD93C75
قاعدة البيانات: BASE
الوصف
ردمك:9781728163901
9781728163895
1728163900
1728163897
DOI:10.1109/ICPS48405.2020.9274727