In order to make robot programming more easy and immediate, walk-through programming techniques can be exploited. However, a modification of a portion of the trajectory usually means to execute the path from the beginning. In this paper we propose a passivity-based framework to modify the trajectory online, manually driving the robot throughout the desired correction. The system follows the initial trajectory, encoded with Dynamical Movement Primitives, by setting high gains in the admittance control. When the human operator grabs the end-effector, the robot becomes compliant and the user can easily teach the desired correction, until he/she releases it at the end of the modification. Finally, the correction is optimally joined to the initial trajectory, restarting the path tracking. To avoid unsafe behaviors, the variation of the admittance parameters is performed exploiting energy tanks, in order to preserve the passivity of the interaction.

A Passivity-Based Strategy for Coaching in Human-Robot Interaction / Landi, Chiara Talignani; Ferraguti, Federica; Fantuzzi, Cesare; Secchi, Cristian. - (2018), pp. 3279-3284. ((Intervento presentato al convegno 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 tenutosi a aus nel 2018 [10.1109/ICRA.2018.8460836].

A Passivity-Based Strategy for Coaching in Human-Robot Interaction

Landi, Chiara Talignani;Ferraguti, Federica;Fantuzzi, Cesare;Secchi, Cristian
2018

Abstract

In order to make robot programming more easy and immediate, walk-through programming techniques can be exploited. However, a modification of a portion of the trajectory usually means to execute the path from the beginning. In this paper we propose a passivity-based framework to modify the trajectory online, manually driving the robot throughout the desired correction. The system follows the initial trajectory, encoded with Dynamical Movement Primitives, by setting high gains in the admittance control. When the human operator grabs the end-effector, the robot becomes compliant and the user can easily teach the desired correction, until he/she releases it at the end of the modification. Finally, the correction is optimally joined to the initial trajectory, restarting the path tracking. To avoid unsafe behaviors, the variation of the admittance parameters is performed exploiting energy tanks, in order to preserve the passivity of the interaction.
2018 IEEE International Conference on Robotics and Automation, ICRA 2018
aus
2018
3279
3284
Landi, Chiara Talignani; Ferraguti, Federica; Fantuzzi, Cesare; Secchi, Cristian
A Passivity-Based Strategy for Coaching in Human-Robot Interaction / Landi, Chiara Talignani; Ferraguti, Federica; Fantuzzi, Cesare; Secchi, Cristian. - (2018), pp. 3279-3284. ((Intervento presentato al convegno 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 tenutosi a aus nel 2018 [10.1109/ICRA.2018.8460836].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1174437
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