Comari, Simone
  
(2023)
A ROS-based software architecture for a versatile collaborative dual-armed autonomous mobile robot for the manufacturing industry, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. 
 Dottorato di ricerca in 
Meccanica e scienze avanzate dell'ingegneria, 35 Ciclo. DOI 10.48676/unibo/amsdottorato/10813.
  
 
  
  
        
        
        
  
  
  
  
  
  
  
    
  
    
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      Abstract
      The industrial context is changing rapidly due to advancements in technology fueled by the Internet and Information Technology. The fourth industrial revolution counts integration, flexibility, and optimization as its fundamental pillars, and, in this context, Human-Robot Collaboration has become a crucial factor for manufacturing sustainability in Europe. Collaborative robots are appealing to many companies due to their low installation and running costs and high degree of flexibility, making them ideal for reshoring production facilities with a short return on investment. 
The ROSSINI European project aims to implement a true Human-Robot Collaboration by designing, developing, and demonstrating a modular and scalable platform for integrating human-centred robotic technologies in industrial production environments. The project focuses on safety concerns related to introducing a cobot in a shared working area and aims to lay the groundwork for a new working paradigm at the industrial level. The need for a software architecture suitable to the robotic platform employed in one of three use cases selected to deploy and test the new technology was the main trigger of this Thesis. The chosen application consists of the automatic loading and unloading of raw-material reels to an automatic packaging machine through an Autonomous Mobile Robot composed of an Autonomous Guided Vehicle, two collaborative manipulators, and an eye-on-hand vision system for performing tasks in a partially unstructured environment. 
The results obtained during the ROSSINI use case development were later used in the SENECA project, which addresses the need for robot-driven automatic cleaning of pharmaceutical bins in a very specific industrial context. The inherent versatility of mobile collaborative robots is evident from their deployment in the two projects with few hardware and software adjustments. 
The positive impact of Human-Robot Collaboration on diverse production lines is a motivation for future investments in research on this increasingly popular field by the industry.
     
    
      Abstract
      The industrial context is changing rapidly due to advancements in technology fueled by the Internet and Information Technology. The fourth industrial revolution counts integration, flexibility, and optimization as its fundamental pillars, and, in this context, Human-Robot Collaboration has become a crucial factor for manufacturing sustainability in Europe. Collaborative robots are appealing to many companies due to their low installation and running costs and high degree of flexibility, making them ideal for reshoring production facilities with a short return on investment. 
The ROSSINI European project aims to implement a true Human-Robot Collaboration by designing, developing, and demonstrating a modular and scalable platform for integrating human-centred robotic technologies in industrial production environments. The project focuses on safety concerns related to introducing a cobot in a shared working area and aims to lay the groundwork for a new working paradigm at the industrial level. The need for a software architecture suitable to the robotic platform employed in one of three use cases selected to deploy and test the new technology was the main trigger of this Thesis. The chosen application consists of the automatic loading and unloading of raw-material reels to an automatic packaging machine through an Autonomous Mobile Robot composed of an Autonomous Guided Vehicle, two collaborative manipulators, and an eye-on-hand vision system for performing tasks in a partially unstructured environment. 
The results obtained during the ROSSINI use case development were later used in the SENECA project, which addresses the need for robot-driven automatic cleaning of pharmaceutical bins in a very specific industrial context. The inherent versatility of mobile collaborative robots is evident from their deployment in the two projects with few hardware and software adjustments. 
The positive impact of Human-Robot Collaboration on diverse production lines is a motivation for future investments in research on this increasingly popular field by the industry.
     
  
  
    
    
      Tipologia del documento
      Tesi di dottorato
      
      
      
      
        
      
        
          Autore
          Comari, Simone
          
        
      
        
          Supervisore
          
          
        
      
        
      
        
          Dottorato di ricerca
          
          
        
      
        
      
        
          Ciclo
          35
          
        
      
        
          Coordinatore
          
          
        
      
        
          Settore disciplinare
          
          
        
      
        
          Settore concorsuale
          
          
        
      
        
          Parole chiave
          robotics, cobots, human-robot collaboration, industry 4.0, machine vision, computer vision, manufacturing industry, safety, 3D scanning, robots, collaborative robots, convolutional neural network, industrial application, pharmaceutical industry, packaging machine, European project, integration
          
        
      
        
          URN:NBN
          
          
        
      
        
          DOI
          10.48676/unibo/amsdottorato/10813
          
        
      
        
          Data di discussione
          20 Giugno 2023
          
        
      
      URI
      
      
     
   
  
    Altri metadati
    
      Tipologia del documento
      Tesi di dottorato
      
      
      
      
        
      
        
          Autore
          Comari, Simone
          
        
      
        
          Supervisore
          
          
        
      
        
      
        
          Dottorato di ricerca
          
          
        
      
        
      
        
          Ciclo
          35
          
        
      
        
          Coordinatore
          
          
        
      
        
          Settore disciplinare
          
          
        
      
        
          Settore concorsuale
          
          
        
      
        
          Parole chiave
          robotics, cobots, human-robot collaboration, industry 4.0, machine vision, computer vision, manufacturing industry, safety, 3D scanning, robots, collaborative robots, convolutional neural network, industrial application, pharmaceutical industry, packaging machine, European project, integration
          
        
      
        
          URN:NBN
          
          
        
      
        
          DOI
          10.48676/unibo/amsdottorato/10813
          
        
      
        
          Data di discussione
          20 Giugno 2023
          
        
      
      URI
      
      
     
   
  
  
  
  
  
    
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