ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-06032019-144502


Tipo di tesi
Tesi di laurea magistrale
Autore
GUGLIOTTA, MARCO
URN
etd-06032019-144502
Titolo
Development and Experimental Validation of Vision, Motion Planning and Control of an Autonomous Dual Arm Robot for Picking and Palletizing
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
Relatori
relatore Prof.ssa Pallottino, Lucia
relatore Garabini, Manolo
Parole chiave
  • vision
  • cnn
  • neural network
  • deep learning
  • robotics
  • manipulation
  • control
  • planning
Data inizio appello
20/06/2019
Consultabilità
Non consultabile
Data di rilascio
20/06/2089
Riassunto
Although production processes have reached a high level of automation, logistics still requires manual operations and hand-operated forklifts that can cause inefficiencies. The challenge that prevents a full logistic automation is given by the high variety of products stored in warehouses, the handling thereof requires extremely flexible automation solutions. This thesis is included in the ILIAD project, which has the design of an autonomous system for de-palletizing and palletizing of heterogeneous goods as one of the main purposes. The solution designed for the project is a dual-arm robotic platform provided with a Pisa/IIT SoftHand and a Velvet Tray. The design of the robot structure and the definition of a set of base functions for the manipulation system have been inspired by the techniques the human operators adopt in Pick-and-Place (P&P) operations. Experiments carried out have shown that the robot is capable to efficiently perform the picking operations in a structured environment scenario, where the type and the position of the objects to manipulate are known and sent to the robot. However, modern robotic solutions have to be robust and effective in unstructured environments and need a perception system. In order to improve robot reliability and achieve a fully autonomous solution, suitable feedback strategies based, e.g., on vision and force feedback have to be implemented.
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