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Robot task plan representation by Petri nets: modelling, identification, analysis and execution

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Abstract

In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.

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Notes

  1. Since we considered that the robot could always see the ball, the Stop action is not actually used in the experiments, so we will not include results concerning this action.

  2. Although predicate SeeBall was always true, we opted to keep it in order to minimise the differences to the task plans running in the real robots.

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Acknowledgements

This work was supported by the Portuguese Fundação para a Ciência e Tecnologia under grant SFRH/BD/12707/2003 and ISR/IST pluriannual funding through the PIDDAC Program funds.

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Correspondence to Pedro Lima.

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Costelha, H., Lima, P. Robot task plan representation by Petri nets: modelling, identification, analysis and execution. Auton Robot 33, 337–360 (2012). https://doi.org/10.1007/s10514-012-9288-x

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