Information Systems Frontiers

, Volume 21, Issue 1, pp 87–107 | Cite as

Integrated Synthesis and Execution of Optimal Plans for Multi-Robot Systems in Logistics

  • Francesco LeofanteEmail author
  • Erika Ábrahám
  • Tim Niemueller
  • Gerhard Lakemeyer
  • Armando Tacchella


Model-based synthesis allows to generate plans to achieve high-level tasks while satisfying certain properties of interest. However, when such plans are executed on concrete systems, several modeling assumptions may be challenged, jeopardizing their real applicability. This paper presents an integrated system for generating, executing and monitoring optimal-by-construction plans for multi-robot systems. This system unites the power of Optimization Modulo Theories with the flexibility of an on-line executive, providing optimal solutions for high-level task planning, and runtime feedback on their feasibility. After presenting how our system orchestrates static and runtime components, we demonstrate its capabilities using the RoboCup Logistics League as testbed. We do not only present our final solution but also its chronological development, and draw some general observations for the development of OMT-based approaches.


Multi-robot systems Optimal task planning Planning as satisfiability Online execution Production logistics 


  1. Ábrahám, E, & Kremer, G. (2016). Satisfiability checking: theory and applications. In Proc. of SEFM’16 (pp. 9–23).Google Scholar
  2. Bensalem, S., Havelund, K., Orlandini, A. (2014). Verification and validation meet planning and scheduling. STTT, 16(1), 1–12.CrossRefGoogle Scholar
  3. Berry, G., & Gonthier, G. (1992). The Esterel synchronous programming language: design, semantics, implementation. Science of Computer Programming, 19(2), 87–152.CrossRefGoogle Scholar
  4. Biere, A., Cimatti, A., Clarke, E.M., Zhu, Y. (1999). Symbolic model checking without BDDs. In Proc. of TACAS’99 (pp. 193–207).Google Scholar
  5. Bjørner, N., Phan, A., Fleckenstein, L. (2015). ν z - An optimizing SMT solver. In Proc. of TACAS’15 (pp. 194—199).Google Scholar
  6. Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., Carrera, A., Palomeras, N., Hurtȯs, N, Carreras, M. (2015). Rosplan: Planning in the robot operating system. In Proc. of ICAPS’15 (pp. 333–341).Google Scholar
  7. Cashmore, M., Fox, M., Long, D., Magazzeni, D. (2016). A compilation of the full PDDL + language into SMT. In Proc. of ICAPS’16 (pp. 79–87).Google Scholar
  8. Cimatti, A., Franzėn, A, Griggio, A., Sebastiani, R., Stenico, C. (2010). Satisfiability modulo the theory of costs: foundations and applications. In Proc. of TACAS’10 (pp. 99–113).Google Scholar
  9. Coles, A., Coles, A.J., Clark, A., Gilmore, S. (2011). Cost-sensitive concurrent planning under duration uncertainty for service-level agreements. In Proc. of ICAPS’11 (pp. 34–41).Google Scholar
  10. Corzilius, F., Kremer, G., Junges, S., Schupp, S., Ȧbrahȧm, E. (2015). SMT-RAT: An open source c+ + toolbox for strategic and parallel SMT solving. In Proc. of SAT’15 (pp. 360–368).Google Scholar
  11. Dantam, N.T., Kingston, Z.K., Chaudhuri, S., Kavraki, L.E. (2016). Incremental task and motion planning: a constraint-based approach. In Proc. of RSS’16.Google Scholar
  12. Dornhege, C., Eyerich, P., Keller, T., Trüg, S, Brenner, M., Nebel, B. (2009). Semantic attachments for domain-independent planning systems. In Proc. of ICAPS’09 (pp. 114–121).Google Scholar
  13. Forgy, C.L. (1982). Rete: a fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence, 19(1), 17– 37.CrossRefGoogle Scholar
  14. Fox, M., & Long, D. (2003). PDDL2.1: An extension to PDDL for expressing temporal planning domains. J Artif Intell Res (JAIR), 20, 61–124.CrossRefGoogle Scholar
  15. Fox, M., Long, D., Magazzeni, D. (2017). Explainable planning. arXiv:1709.10256.
  16. Giarratano, J.C. (2007). CLIPS Reference Manuals.
  17. Hofmann, T., Niemueller, T., Claßen, J, Lakemeyer, G. (2016). Continual planning in Golog. In Proc. of AAAI’16 (pp. 3346–3353).Google Scholar
  18. Ingham, M., Ragno, R., Williams, B. (2001). A reactive model-based programming language for robotic space explorers. In Proc. of i-SAIRAS’01.Google Scholar
  19. Ingrand, F.F., Chatila, R., Alami, R., Robert, F. (1996). PRS: A high level supervision and control language for autonomous mobile robots. In Proc. of ICRA’96 (pp. 43–49).Google Scholar
  20. Leofante, F. (2018). Guaranteed plans for multi-robot systems via Optimization Modulo Theories. In Proc. of AAAI’18.Google Scholar
  21. Leofante, F., Ábrahám, E, Niemueller, T., Lakemeyer, G., Tacchella, A. (2017). On the synthesis of guaranteed-quality plans for robot fleets in logistics scenarios via optimization modulo theories. In Procof IRI’17 (pp. 403–410).Google Scholar
  22. McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D. (1998). PDDL – The Planning Domain Definition Language. Tech. rep., AIPS-98 Planning Competition Committee.Google Scholar
  23. Nedunuri, S., Prabhu, S., Moll, M., Chaudhuri, S., Kavraki, L.E. (2014). SMT-Based synthesis of integrated task and motion plans from plan outlines. In Proc. of ICRA’14 (pp. 655–662).Google Scholar
  24. Niemueller, T., Ferrein, A., Lakemeyer, G. (2009). A Lua-based behavior engine for controlling the humanoid robot Nao. In RoboCup Symposium (p. 2009).Google Scholar
  25. Niemueller, T., Lakemeyer, G., Ferrein, A. (2013). Incremental task-level reasoning in a competitive factory automation scenario. In Proc. of AAAI’13 Spring Symposium.Google Scholar
  26. Niemueller, T., Lakemeyer, G., Ferrein, A. (2015). The RoboCup Logistics League as a benchmark for planning in robotics. In Proc. of PlanRob@ICAPS’15.Google Scholar
  27. Niemueller, T., Karpas, E., Vaquero, T., Timmons, E. (2016a). Planning competition for logistics robots in simulation. In Proc. of PlanRob@ICAPS’16.Google Scholar
  28. Niemueller, T., Neumann, T., Henke, C., Schönitz, S, Reuter, S., Ferrein, A., Jeschke, S., Lakemeyer, G. (2016b). Improvements for a robust production in the RoboCup Logistics League 2016. In Proc. of RoboCup’16 (pp. 589–600).Google Scholar
  29. Niemueller, T, Lakemeyer, G, Leofante, F, Ábrahám, E. (2017). Towards CLIPS-based Task Execution and Monitoring with SMT-based Decision Optimization. In: Proc. of PlanRob@ICAPS’17.Google Scholar
  30. Nieuwenhuis, R., & Oliveras, A. (2006). On SAT modulo theories and optimization problems. In Proc. of SAT’06 (pp. 156–169).Google Scholar
  31. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y. (2009). ROS: An open-source robot operating system. In ICRA workshop on open source software, (Vol. 3 p. 5).Google Scholar
  32. RCLL Technical Committee. (2017). RoboCup Logistics League – Rules and regulations 2017.Google Scholar
  33. Saha, I., Ramaithitima, R., Kumar, V., Pappas, G.J., Seshia, S.A. (2014). Automated composition of motion primitives for multi-robot systems from safe LTL specifications. In Proc. of IROS’14 (pp. 1525–1532).Google Scholar
  34. Sebastiani, R., & Tomasi, S. (2015). Optimization modulo theories with linear rational costs. ACM Transactions on Computational Logic, 16(2), 12:1–12:43.CrossRefGoogle Scholar
  35. Sebastiani, R., & Trentin, P. (2015a). OptimathSAT: A tool for optimization modulo theories. In Proc. of CAV’15 (pp. 447–454).Google Scholar
  36. Sebastiani, R., & Trentin, P. (2015b). Pushing the envelope of optimization modulo theories with linear-arithmetic cost functions. In Proc. of TACAS’15 (pp. 335–349).Google Scholar
  37. Verma, V., Estlin, T., Jónsson, A., Pasareanu, C., Simmons, R., Tso, K. (2005a). Plan execution interchange language (PLEXIL) for executable plans and command sequences. In Proc. of i-SAIRAS’05.Google Scholar
  38. Verma, V., Jónsson, A., Simmons, R., Estlin, T., Levinson, R. (2005b). Survey of command execution systems for NASA spacecraft and robots. In Plan execution: a reality check, workshop at ICAPS’05.Google Scholar
  39. Verma, V., Jónsson, A., Pasareanu, C., Iatauro, M. (2006). Universal executive and PLEXIL: Engine and language for robust spacecraft control and operations. In American institute of aeronautics and astronautics space.Google Scholar
  40. Wang, Y., Dantam, N.T., Chaudhuri, S., Kavraki, L.E. (2016). Task and motion policy synthesis as liveness games. In Proc. of ICAPS’16 (p. 536).Google Scholar
  41. Wygant, R.M. (1989). CLIPS: A powerful development and delivery expert system tool. Computers & Industrial Engineering, 17, 1–4.CrossRefGoogle Scholar
  42. Zwilling, F., Niemueller, T., Lakemeyer, G. (2014). Simulation for the RoboCup Logistics League with real-world environment agency and multi-level abstraction. In Robot Soccer World Cup (pp. 220–232). Springer.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Theory of Hybrid SystemsRWTH Aachen UniversityAachenGermany
  2. 2.Knowledge-Based SystemsRWTH Aachen UniversityAachenGermany
  3. 3.Università degli Studi di GenovaGenovaItaly

Personalised recommendations