The synergy between the robot’s abilities and human expertise provides several advantages in the industrial processes. A proper integration of Task and Motion Planning (TAMP), considering the interaction of the user with the environment is essential to maximize the benefits of robot-assisted tasks and to ensure safety in collaborative H-R tasks. However, the state of the art of the TAMP research field should be improved to overcome the limitations of the traditional TAMP approaches to be used in industrial scenarios. We illustrate a list of challenges to be solved in order to improve to manage efficiently the dynamism and uncertainty given by introducing a human operator into a robotized process of draping fiber carbon plies, with reference to the granted European Project DrapeBot. Finally, the proposed TAMP architecture is presented to address the challenges described.

Integrating Task and Motion Planning for Manufacturing Processes

Alberto Gottardi
;
Nicola Castaman;Emanuele Menegatti
2023

Abstract

The synergy between the robot’s abilities and human expertise provides several advantages in the industrial processes. A proper integration of Task and Motion Planning (TAMP), considering the interaction of the user with the environment is essential to maximize the benefits of robot-assisted tasks and to ensure safety in collaborative H-R tasks. However, the state of the art of the TAMP research field should be improved to overcome the limitations of the traditional TAMP approaches to be used in industrial scenarios. We illustrate a list of challenges to be solved in order to improve to manage efficiently the dynamism and uncertainty given by introducing a human operator into a robotized process of draping fiber carbon plies, with reference to the granted European Project DrapeBot. Finally, the proposed TAMP architecture is presented to address the challenges described.
2023
Italian Workshop on Artificial Intelligence and Robotics (AIRO) at AIxIA
Italian Workshop on Artificial Intelligence and Robotics (AIRO) at AIxIA
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3502420
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