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ANDREA PUPA
Assegnista di ricerca
Dipartimento di Scienze e Metodi dell'Ingegneria
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Pubblicazioni
2024
- A Dynamic Planner for Safe and Predictable Human-Robot Collaboration
[Articolo su rivista]
Pupa, A.; Minelli, M.; Secchi, C.
abstract
2022
- A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration
[Articolo su rivista]
Pupa, A.; Van Dijk, W.; Brekelmans, C.; Secchi, C.
abstract
Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot.
2021
- A Dynamic Architecture for Task Assignment and Scheduling for Collaborative Robotic Cells
[Capitolo/Saggio]
Pupa, A.; Landi, C. T.; Bertolani, M.; Secchi, C.
abstract
In collaborative robotic cells, a human operator and a robot share the workspace in order to execute a common job, consisting of a set of tasks. A proper allocation and scheduling of the tasks for the human and for the robot is crucial for achieving an efficient human-robot collaboration. In order to deal with the dynamic and unpredictable behavior of the human and for allowing the human and the robot to negotiate about the tasks to be executed, a two layers architecture for solving the task allocation and scheduling problem is proposed. The first layer optimally solves the task allocation problem considering nominal execution times. The second layer, which is reactive, adapts online the sequence of tasks to be executed by the robot considering deviations from the nominal behaviors and requests coming from the human and from robot. The proposed architecture is experimentally validated on a collaborative assembly job.
2021
- A Human-Centered Dynamic Scheduling Architecture for Collaborative Application
[Articolo su rivista]
Pupa, A.; Van Dijk, W.; Secchi, C.
abstract
In collaborative robotic applications, human and robot have to work together during a whole shift for executing a sequence of jobs. The performance of the human robot team can be enhanced by scheduling the right tasks to the human and the robot. The scheduling should consider the task execution constraints, the variability in the task execution by the human, and the job quality of the human. Therefore, it is necessary to dynamically schedule the assigned tasks. In this letter, we propose a two-layered architecture for task allocation and scheduling in a collaborative cell. Job quality is explicitly considered during the allocation of the tasks and over a sequence of jobs. The tasks are dynamically scheduled based on the real time monitoring of the human's activities. The effectiveness of the proposed architecture is experimentally validated.
2021
- A Safety-Aware Architecture for Task Scheduling and Execution for Human-Robot Collaboration
[Relazione in Atti di Convegno]
Pupa, A.; Secchi, C.
abstract
In collaborative robotic applications, human and robot have to work together to accomplish a common job, composed by a set of tasks. In order to achieve an efficient human-robot collaboration (HRC), it is important to have an integration between a proper task scheduling strategy and a task execution strategy. The first must deal with the variability of the two agents, while the second must deal with the safety standards. In this paper, we propose an integrated architecture for task scheduling and execution in a collaborative cell. The tasks are dynamically scheduled handling the uncertainity in both the human and the robot behaviors. Subsequently, at the execution level, the task is accomplished computing trajectories comply with the safety regulations. The planning information are mutually integrated in real-time with the scheduling procedure in order improve the HRC.
2021
- A Safety-Aware Kinodynamic Architecture for Human-Robot Collaboration
[Articolo su rivista]
Pupa, A.; Arrfou, M.; Andreoni, G.; Secchi, C.
abstract
The new paradigm of human-robot collaboration has led to the creation of shared work environments in which humans and robots work in close contact with each other. Consequently, the safety regulations have been updated addressing these new scenarios. The mere application of these regulations may lead to a very inefficient behavior of the robot. In order to preserve safety for the human operators and allow the robot to reach a desired configuration in a safe and efficient way, a two layers architecture for trajectory planning and scaling is proposed. The first layer calculates the nominal trajectory and continuously adapts it based on the human behavior. The second layer, which explicitly considers the safety regulations, scales the robot velocity and requests for a new trajectory if the robot speed drops. The proposed architecture is experimentally validated on a Pilz PRBT manipulator.