Task agility is an increasingly desirable feature for robots in application domains such as manufacturing. The Canonical Robot Command Language (CRCL) is a lightweight information model built for agile tasking of robotic systems. CRCL replaces the underlying complex proprietary robot programming interface with a standard interface. In this paper, we exchange the automated planning component that CRCL used in the past for a rational agent in the GWENDOLEN agent programming language, thus providing greater possibilities for formal verification and explicit autonomy. We evaluate our approach by performing agile tasking in a kitting case study

Agile Tasking of Robotic Systems with Explicit Autonomy / Cardoso, Rafael C.; Michaloski, John L.; Schlenoff, Craig; Ferrando, Angelo; Dennis, Louise A.; Fisher, Michael. - 34:(2021), pp. 0-0. (Intervento presentato al convegno 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021 tenutosi a usa nel 2021) [10.32473/flairs.v34i1.128481].

Agile Tasking of Robotic Systems with Explicit Autonomy

Angelo Ferrando;
2021

Abstract

Task agility is an increasingly desirable feature for robots in application domains such as manufacturing. The Canonical Robot Command Language (CRCL) is a lightweight information model built for agile tasking of robotic systems. CRCL replaces the underlying complex proprietary robot programming interface with a standard interface. In this paper, we exchange the automated planning component that CRCL used in the past for a rational agent in the GWENDOLEN agent programming language, thus providing greater possibilities for formal verification and explicit autonomy. We evaluate our approach by performing agile tasking in a kitting case study
2021
34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021
usa
2021
34
0
0
Cardoso, Rafael C.; Michaloski, John L.; Schlenoff, Craig; Ferrando, Angelo; Dennis, Louise A.; Fisher, Michael
Agile Tasking of Robotic Systems with Explicit Autonomy / Cardoso, Rafael C.; Michaloski, John L.; Schlenoff, Craig; Ferrando, Angelo; Dennis, Louise A.; Fisher, Michael. - 34:(2021), pp. 0-0. (Intervento presentato al convegno 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021 tenutosi a usa nel 2021) [10.32473/flairs.v34i1.128481].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1331813
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