The robotic industry needs new, innovative, ideas to be globally competitive. Conventional industrial robots are not able to adapt to changes in the assembly processes. Flexible assembly applications are actually uncommon and only isolated attempts exploit industrial robots to perform tasks with variability in the parts. Variability aspects are emphasized when developing novel manufacturing applications involving human–robot collaboration which are the foundation of Industry 4.0 systems. In this chapter, we describe how variability can be considered and mathematically described as part of the problem to obtain a flexible robotic solution. The selected approach is based on a probabilistic representation of the task obtained starting from a set of demonstrations collected from humans. The chapter illustrates the different steps leading to the complete learning framework. We start by describing the strategies adopted during the data collection phase. From the raw data, the design of feature extraction procedures is provided alongside a set of preprocessing techniques used to remove noisy and incoherent information. The resulting data set is used to train a model of task by following a probabilistic approach. The output of the model is exploited to actuate an industrial manipulator in the context of significant production scenarios. The robot motion strategies are also analyzed depending on the level of flexibility requested from the specific use case. Two main use cases are introduced: (1) the automatic assembly of a car door with its module, and (2) the robotized manufacturing process of electric machines, in particular winding of coils on stator or rotor cores. Each problem is mathematically formulated by modeling both the robotic platform and the target. The influence of the scenario variability with respect to the computed robotic motion is considered. The system flexibility is evaluated by means of an extensive set of benchmarking tests by recording data and actuating robots in both simulated and real environments. Achievements are compared with respect to state-of-the-art solutions by defining a set of objectives and metrics. The goal is to measure the performance of the system, for example, in minimizing the time and energy needed to move the robot in the working space, in generating an effective human–robot interaction with low reaction time and high accuracy, and in providing an intuitive robot learning technique to easily allow the human to teach the robot new tasks. Dynamic online reconfigurability of the framework is considered by testing its capability to deal with novel situations and new products. The integration of the proposed technologies with current robotic systems is discussed and a solution based on the robot operating system is proposed to provide a good infrastructure for network communication as well as all the tools necessary for a modern distributed and heterogeneous system. The feasibility and cost-effectiveness of the developed solutions are taken into account in order to demonstrate the applicability of the proposed approach in actual industrial settings.

A probabilistic approach to reconfigurable interactive manufacturing and coil winding for Industry 4.0

Stefano Michieletto
;
Francesca Stival;Enrico Pagello
2021

Abstract

The robotic industry needs new, innovative, ideas to be globally competitive. Conventional industrial robots are not able to adapt to changes in the assembly processes. Flexible assembly applications are actually uncommon and only isolated attempts exploit industrial robots to perform tasks with variability in the parts. Variability aspects are emphasized when developing novel manufacturing applications involving human–robot collaboration which are the foundation of Industry 4.0 systems. In this chapter, we describe how variability can be considered and mathematically described as part of the problem to obtain a flexible robotic solution. The selected approach is based on a probabilistic representation of the task obtained starting from a set of demonstrations collected from humans. The chapter illustrates the different steps leading to the complete learning framework. We start by describing the strategies adopted during the data collection phase. From the raw data, the design of feature extraction procedures is provided alongside a set of preprocessing techniques used to remove noisy and incoherent information. The resulting data set is used to train a model of task by following a probabilistic approach. The output of the model is exploited to actuate an industrial manipulator in the context of significant production scenarios. The robot motion strategies are also analyzed depending on the level of flexibility requested from the specific use case. Two main use cases are introduced: (1) the automatic assembly of a car door with its module, and (2) the robotized manufacturing process of electric machines, in particular winding of coils on stator or rotor cores. Each problem is mathematically formulated by modeling both the robotic platform and the target. The influence of the scenario variability with respect to the computed robotic motion is considered. The system flexibility is evaluated by means of an extensive set of benchmarking tests by recording data and actuating robots in both simulated and real environments. Achievements are compared with respect to state-of-the-art solutions by defining a set of objectives and metrics. The goal is to measure the performance of the system, for example, in minimizing the time and energy needed to move the robot in the working space, in generating an effective human–robot interaction with low reaction time and high accuracy, and in providing an intuitive robot learning technique to easily allow the human to teach the robot new tasks. Dynamic online reconfigurability of the framework is considered by testing its capability to deal with novel situations and new products. The integration of the proposed technologies with current robotic systems is discussed and a solution based on the robot operating system is proposed to provide a good infrastructure for network communication as well as all the tools necessary for a modern distributed and heterogeneous system. The feasibility and cost-effectiveness of the developed solutions are taken into account in order to demonstrate the applicability of the proposed approach in actual industrial settings.
Advances in Mathematics for Industry 4.0
978-0-12-818906-1
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3358829
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact