Definitions
A robot task can be described as a piece of work the robot is in charge of and must carry out. In most cases, one assumes some representation of the state of the world (which is a conceptualization of reality, adequate for the task in hand, without adding unnecessary complexity), and the robot task consists of making the system evolve from the current world state to a desired goal state, by executing the appropriate actions. The sequence of actions carried out to execute a task is called a plan, and the actions can be further decomposed into sub-actions, and those into sub-sub-actions and so on, until one reaches an atomic action which is not further decomposable, often designated as primitive actions. There are no universally accepted standard definitions, for tasks, plans, actions and primitive actions. Different authors use different plan decompositions and names for the...
References
Belta C, Bicchi A, Egerstedt M, Frazzoli E, Klavins E, Pappas G (2007) Symbolic planning and control of robot motion [grand challenges of robotics]. IEEE Robot Autom Mag 14(1):61–70
Cassandras CG, Lafortune S (2008) Introduction to discrete event systems, 2nd edn. Springer
Chen Y, Tumová J, Belta C (2012) LTL robot motion control based on automata learning of environmental dynamics. In: Proceedings of ICRA’12: the 2012 IEEE international conference on robotics and automation. IEEE, St. Paul, pp 5177–5182
Cimatti A, Pistore M, Roveri M, Traverso P (2003) Weak, strong, and strong cyclic planning via symbolic model checking. Artif Intell 147(1–2):35–84
Cizelj I, Belta C (2013) Control of noisy differential-drive vehicles from time-bounded temporal logic specifications. In: Procedings of ICRA’13: the 2013 IEEE international conference on robotics and automation, Karlsruhe
Costelha H, Lima P (2007) Modeling, analysis and execution of robotic tasks using Petri nets. In: Proceedings of IROS 2007—IEEE international conference on intelligent robots and systems, San Diego, pp 1449–1454
Costelha H, Lima PU (2012) Robot task plan representation by petri nets: modelling, identification, analysis and execution. Autonom Robot, 33(4):337–360
De Giacomo G, Vardi M.Y. Automata-theoretic approach to planning for temporally extended goals. European Conference on Planning. Springer, Berlin, Heidelberg, 1999.
Espiau B, Kapellos K, Jourdan M, Simon D (1995) On the validation of robotics control systems part I: high level specification and formal verification. Technical Report 2719, INRIA
Fainekos G, Kress-Gazit H, Pappas G (2005) Temporal logic motion planning for mobile robots. In: Proceedings of ICRA’05: the 2005 IEEE international conference on robotics and automation. IEEE, Barcelona, pp 2020–2025
Huth M, Ryan M (2006) Introduction to automata theory, languages, and computation, 3rd edn. Addison-Wesley Longman Publishing Co. Inc., Boston
Johnson B, Havlak F, Campbell M, Kress-Gazit H (2012) Execution and analysis of high-level tasks with dynamic obstacle anticipation. In: Procedings of ICRA’12: the 2012 IEEE international conference on robotics and automation. IEEE, St. Paul, pp 330–337
Kaelbling LP, Littman ML, Cassandra AR (1998) Planning and acting in partially observable stochastic domains. Artif Intell 1(101):99–134
Kim G, Chung W (2007) Navigation behavior selection using generalized stochastic Petri nets for a service robot. IEEE Trans Syst Man Cybern Part C: Appl Rev 37(4):494–503
King J, Pretty R, Gosine R (2003) Coordinated execution of tasks in a multiagent environment. IEEE Trans Syst Man Cybern Part A: Syst Humans 33(5):615–619
Kloetzer M, Belta C (2008a) Distributed implementations of global temporal logic motion specifications. In: ICRA’08: Proceedings of the 2008 IEEE international conference on robotics and automation, Pasadena, pp 393–398
Kloetzer M, Belta C (2008b) A fully automated framework for control of linear systems from temporal logic specifications. IEEE Trans Autom Control 53(1): 287–297
Kloetzer M, Mahulea C (2016) Multi-robot path planning for syntactically co-safe LTL specifications. In: 13th international workshop on discrete event systems, pp 452–458
Kober J, Bagnell JA, Peters J (2009) Reinforcement learning in robotics: a survey. Int J Robot, 32(11):1238–1274
Košecká J (1996) A framework for modeling and verifying visually guided agents: design, analysis and experiments. PhD thesis, GRASP Laboratory, University of Pennsylvania
Kosecka J, Bogoni L (1994) Application of discrete events systems for modeling and controlling robotic agents. In: Proceedings of ICRA’94: the IEEE international conference on robotics and automation. IEEE, pp 2557–2562
Kress-Gazit H, Fainekos GE, Pappas GJ (2007) From structured english to robot motion. In: Proceedings of IROS’07: IEEE/RSJ international conference on intelligent robots and systems, San Diego, pp 2717–2722
Kress-Gazit H, Fainekos GE, Pappas GJ (2009) Temporal logic-based reactive mission and motion planning. IEEE Trans Robot 25(6):1370–1381
Lacerda B, Lima PU (2011) Designing Petri net supervisors for from LTL specifications. In: Proceedings of RSS VII—the 2011 robotics: science and systems conference, Los Angeles
Lahijanian M, Andersson S, Belta C (2009) A probabilistic approach for control of a stochastic system from LTL specifications. In: Proceedings of CDC’09: the 48th IEEE conference on decision and control. IEEE, Shanghai, pp 2236–2241
Lee JS, Zhou MC, Hsu PL (2005) An application of Petri nets to supervisory control for human-computer interactive systems. IEEE Trans Ind Electron 52(5):1220–1226
Loizou S, Kyriakopoulos K (2004) Automatic synthesis of multi-agent motion tasks based on LTL specifications. In: Proceedings of CDC’04: the 43rd IEEE conference on decision and control. IEEE, Paradise Island, pp 153–158
Messias JV, Spaan MT, Lima PU (2013) Multiagent pomdps with asynchronous execution. In: Proceedings of the 2013 international conference on autonomous agents and multi-agent systems. International foundation for autonomous agents and multiagent systems
Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77(4):541–580
Petri CA (1966) Kommunikation mit automaten. Technical report. Institut für Instrumentelle Mathematik, Bonn. English translation
Pistore M, Traverso P (2001) Planning as model checking extended goals in non-deterministic domains. In: Proceedings of IJCAI’01: the 17th international joint conference on artificial intelligence, Seattle, pp 479–484
Ramadge PG, Wonham W (1989) The control of discrete event systems. Proc IEEE 77(1):81–98
Raman V, Kress-Gazit H (2012) Automated feedback for unachievable high-level robot behaviors. In: Procedings of ICRA’12: the 2012 IEEE international conference on robotics and automation. IEEE, St. Paul, pp 5156–5162
Raman V, Kress-Gazit H (2013) Explaining impossible high-level robot behaviors. IEEE Trans Robot 29(1):94–104
Ricker S, Sarkar N, Rudiet K (1996) A discrete-event systems approach to modeling dextrous manipulation. Robotica 14(05):515–525
Russell SJ, Norvig P (2014) Artificial intelligence: a modern approach, 3rd edn. Pearson Education
Sarid S, Xu B, Kress-Gazit H (2012) Guaranteeing high-level behaviors while exploring partially known maps. In: Proceedings of robotics: science and systems, Sydney
Schaft AJVD, Schumacher JM (2000) An introduction to hybrid dynamical systems, vol 251. Springer, London
Smith S, Tůmová J, Belta C, Rus D (2011) Optimal path planning for surveillance with temporal-logic constraints. Int J Robot Res 30(14):1695–1708
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, 1st edn. MIT press, Cambridge
Thabet M, Montebelli A, Kyrki V (2016) Learning movement synchronization in multi-component robotic systems. In: 2016 IEEE international conference on robotics and automation (ICRA). https://doi.org/10.1109/ICRA.2016.7487141
Ulusoy A, Smith S, Belta C (2012a) Optimal multi-robot path planning with LTL constraints: guaranteeing correctness through synchronization. In: Proceedings of DARS: 2012 international symposium on distributed autonomous robotic systems, Baltimore
Ulusoy A, Smith S, Ding X, Belta C (2012b) Robust multi-robot optimal path planning with temporal logic constraints. In: Proceedings of ICRA’12: the 2012 IEEE international conference on robotics and automation. IEEE, St. Paul, pp 4693–4698
Ulusoy A, Wongpiromsarn T, Belta C (2012c) Incremental control synthesis in probabilistic environments with temporal logic constraints. In: Proceedings of CDC: the 2012 IEEE conference on decision and control, Maui
Veiga T, Miraldo P, Ventura R, Lima PU (2016) Efficient object search for mobile robots in dynamic environments: semantic map as an input for the decision maker. In: Proceedings of IROS 2016—IEEE/RSJ international conference on intelligent robots and systems, Daejeon
Viswanadham N, Narahari Y (1992) Performance modeling of automated manufacturing systems. Prentice Hall
Wang F, Kyriakopoulos K, Tsolkas A, Saridis G (1991) A Petri-net coordination model for an intelligent mobile robot. Trans IEEE Syst Man Cybern Soc 21(4)
Wolff E, Topcu U, Murray R (2012) Optimal control with weighted average costs and temporal logic specifications. In: Proceedings of RSS VIII—the 2012 robotics: science and systems conference, Sydney
Zhou M, Venkatesh K (1999) Modeling, simulation and control of flexible manufacturing systems. World Scientific Publishing
Ziparo VA, Iocchi L (2006) Petri net plans. In: Proceedings of the fourth international workshop on modelling of objects, components, and agents (MOCA’06). University of Hamburg, pp 267–290
Ziparo VA, Iocchi L, Lima PU, Nardi D, Palamara PF (2011) Petri net plans: a framework for collaboration and coordination in multi-robot systems. J Autonom Agents Multi-Agent Syst 23(3):344–383
Acknowledgements
This work was supported by the FCT project LARSyS - FCT Project UIDB/50009/2020.
Special thanks to my former PhD students Bruno Lacerda and Hugo Costelha, who contributed, directly or indirectly, to this text. Thanks also to Davide Brugali for the interesting discussions that lead to the invitation to write this chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2020 Springer-Verlag GmbH Germany, part of Springer Nature
About this entry
Cite this entry
Lima, P.U. (2020). Robot Task Modeling. In: Ang, M., Khatib, O., Siciliano, B. (eds) Encyclopedia of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41610-1_9-1
Download citation
DOI: https://doi.org/10.1007/978-3-642-41610-1_9-1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41610-1
Online ISBN: 978-3-642-41610-1
eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering