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.
Similar content being viewed by others
Notes
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.
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.
References
Akin, H. L., Birk, A., Bonarini, A., Kraetzschmar, G., Lima, P., Nardi, D., Pagello, E., Reggiani, M., Saffiotti, A., Sanfeliu, A., & Spaan, M. (2008) White paper on network robot systems, and formal models and methods for cooperation. http://aass.oru.se/Agora/EuronCoop/Docs/SIG_CoopRob_WhitePaper.pdf.
Barbosa, M., Ramos, N., & Lima, P. (2007). MeRMaID - multiple-robot middleware for intelligent decision-making. In 6th IFAC symposium on intelligent autonomous vehicles IAV2007 Amsterdam: Elsevier.
Basu, A., Gallien, M., Lesire, C., Nguyen, T. H., Bensalem, S., Ingrand, F., & Sifakis, J. (2008). Incremental component-based construction and verification of a robotic system. In Proc. of the 2008 European conference on artificial intelligence (ECAI 2008) (24–29).
Bause, F., & Kritzinger, P. S. (2002). Stochastic Petri nets: an introduction to the theory (2nd edn.). Berlin: Vieweg Verlag.
Bernardinello, L., & Cindio, F. D. (1992). A survey of basic net models and modular net classes. In Advances in Petri nets 1992, the DEMON project (304–351). Berlin: Springer.
Cassandras, C. G., & Lafortune, S. (2008). Introduction to discrete event systems (2nd edn.). New York: Springer.
Castelnuovo, A., Ferrarini, L., & Piroddi, L. (2007). An incremental petri net-based approach to the modeling of production sequences in manufacturing systems. IEEE Transactions on Automation Science and Engineering, 4(3), 424–434.
Costelha, H., & Lima, P. (2008). Modelling, analysis and execution of multi-robot tasks using petri nets. In Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, international foundation for autonomous agents and multiagent systems (AAMAS ’08) (pp. 1187–1190).
Dideban, A., & Alla, H. (2008). Reduction of constraints for controller synthesis based on safe Petri nets. Automatica, 7(7), 1697–1706.
Dupont, P., Denis, F., & Esposito, Y. (2005). Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms. Pattern Recognition, 38(9), 1349–1371.
Espiau, B., Kapellos, K., Jourdan, M., & Simon, D. (1995). On the validation of robotics control systems. Part I. High level specification and formal verification. Tech. rep. 2719, INRIA.
Fikes, R., & Nilsson, N. J. (1971). STRIPS: a new approach to the application of theorem proving. Artificial Intelligence, 2(3), 189–208.
Girault, C., & Valk, R. (2003). Petri nets for systems engineering. Berlin: Springer.
Herrero-Perez, D., & Martinez-Barbera, H. (2008). Petri Nets based coordination of flexible autonomous guided vehicles in flexible manufacturing systems. In IEEE int. conf. on emerging technologies and factory automation (ETFA 2008) (pp. 508–515).
Hickmott, S., Rintanen, J., Thiébaux, S., & White, L. (2007). Planning via petri net unfolding. In Proc. of the 20th int. joint conf. on artificial intelligence (IJCAI’07) (pp. 1904–1911). San Mateo: Morgan Kaufmann.
Jarvis, J., & Shier, D. (1999). Graph-theoretic analysis of finite Markov chains. In Applied mathematical modeling: a multidisciplinary approach Boca Raton: CRC Press.
Kim, G., & Chung, W. (2007). Navigation behavior selection using generalized stochastic Petri nets for a service robot. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews, 37(4), 494–503.
King, J., Pretty, R., & Gosine, R. (2003) Coordinated execution of tasks in a multiagent environment. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 615–619.
Košecká, J., Christensen, H. I., & Bajcsy, R. (1997). Experiments in behaviour composition. Robotics and Autonomous Systems, 19(3–4), 287–298.
Kress-Gazit, H., Fainekos, G. E., & Pappas, G. J. (2009). Temporal logic-based reactive mission and motion planning. IEEE Transactions on Robotics, 25(6), 1370–1381.
Mauser, S., & Lorenz, R. (2009). Variants of the language based synthesis problem for Petri nets. In Ninth int. conf. on application of concurrency to system design (pp. 89–98).
Michel, O. (1998) Webots—fast prototyping and simulation of mobile robots.
Murata, T. (1989). Petri nets: properties, analysis and applications. Proceedings of the IEEE, 4, 541–580.
Ocasio, VA (2009). Stability of boolean dynamical systems and graph periodicity. Master’s thesis, University of Puerto Rico, Mayagüez Campus.
Petri, C. A. (1966). Kommunikation mit automaten. Tech. rep., Bonn: Institut für Instrumentelle Mathematik, english translation.
Puterman, M. L. (1994). Markov decision processes—discrete stochastic dynamic programming (1st edn.). New York: Wiley.
Qin, Y., & Xu, R. (2009). GSPN-based modeling and analysis for robotized assembly system. In IEEE int. conf. on robotics and biomimetics, 2008 (ROBIO 2008) (pp. 1070–1075).
Röck, A., & Kresman, R. (2006). On Petri nets and predicate-transition nets. In Proceedings of the international conference on software engineering research and practice & conference on programming languages and compilers, (SERP 2006) (pp. 903–909).
Toktam Ebadi, M. P., & Purvis, M. (2009) A framework for facilitating cooperation in multi-agent systems. The Journal of Supercomputing.
Viswanadham, N., & Narahari, Y. (1992). Performance modeling of automated manufacturing systems. New York: Prentice Hall.
Wang, F., Kyriakopoulos, K., Tsolkas, A., & Saridis, G. (1991). A Petri-net coordination model for an intelligent mobile robot. IEEE Transactions on Systems Science and Cybernetics, 21(4), 777–789.
Younes, H. L. S. & Littman, M. L. (2004). PPDDL1.0: An extension to PDDL for expressing planning domains with probabilistic effects. Tech. rep. CMU-CS-04-167, Carnegie Mellon University.
Zhou, M., & Venkatesh, K. (1999). Modeling, simulation and control of flexible manufacturing systems. Singapore: World Scientific Publishing.
Ziparo, VA, & Iocchi, L. (2006). Petri net plans. In Proc. of the fourth int. workshop on modelling of objects, components, and agents (MOCA’06) (pp. 267–290). Hamburg: University of Hamburg.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-012-9288-x