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Autonomous Robots

, Volume 43, Issue 1, pp 213–238 | Cite as

Finding optimal feasible global plans for multiple teams of heterogeneous robots using hybrid reasoning: an application to cognitive factories

  • Zeynep G. Saribatur
  • Volkan Patoglu
  • Esra ErdemEmail author
Article
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Abstract

We consider cognitive factories with multiple teams of heterogenous robots, and address two key challenges of these domains, hybrid reasoning for each team and finding an optimal global plan (with minimum makespan) for multiple teams. For hybrid reasoning, we propose modeling each team’s workspace taking into account capabilities of heterogeneous robots, embedding continuous external computations into discrete symbolic representation and reasoning, not only optimizing the makespans of local plans but also minimizing the total cost of robotic actions. To find an optimal global plan, we propose a semi-distributed approach that does not require exchange of information between teams but yet achieves on an optimal coordination of teams that can help each other. We prove that the optimal coordination problem is NP-complete, and describe a solution using automated reasoners. We experimentally evaluate our methods, and show their applications on a cognitive factory with dynamic simulations and a physical implementation.

Keywords

AI reasoning methods Optimal global planning Hybrid reasoning Coordination of multiple teams Intelligent and flexible manufacturing 
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References

  1. Alami, R., Ingrand, F., & Qutub, S. (1998). A scheme for coordinating multi-robots planning activities and plans execution. In Proceedings of ECAI.Google Scholar
  2. Bonisoli, A., Gerevini, A. E., Saetti, A., & Serina, I. (2014). A privacy-preserving model for the multi-agent propositional planning problem. In Proceedings of ECAI (pp. 973–974).Google Scholar
  3. Brafman, R. I. (2015). A privacy preserving algorithm for multi-agent planning and search. In Proceedings of IJCAI (pp. 1530–1536).Google Scholar
  4. Brafman, R. I., & Domshlak, C. (2008). From one to many: Planning for loosely coupled multi-agent systems. In Proceedings of ICAPS (pp. 28–35).Google Scholar
  5. Brewka, G., Eiter, T., & Truszczynski, M. (2011). Answer set programming at a glance. Communications of the ACM, 54(12), 92–103.CrossRefGoogle Scholar
  6. Caldiran, O., Haspalamutgil, K., Ok, A., Palaz, C., Erdem, E., & Patoglu, V. (2009). Bridging the gap between high-level reasoning and low-level control. In Proceedings of LPNMR.Google Scholar
  7. Canny, J. F. (1988). The complexity of robot motion planning. Cambridge, MA: MIT Press.zbMATHGoogle Scholar
  8. Chevaleyre, Y., Dunne, P. E., Endriss, U., Lang, J., Lemaître, M., Maudet, N., et al. (2006). Issues in multiagent resource allocation. Informatica, 30(1), 3–31.zbMATHGoogle Scholar
  9. Dantam, N. T., Kingston, Z. K., Chaudhuri, S., & Kavraki, L. E. (2016). Incremental task and motion planning: A constraint-based approach. In Proceedings of RSS.Google Scholar
  10. Dantsin, E., Eiter, T., Gottlob, G., & Voronkov, A. (2001). Complexity and expressive power of logic programming. ACM Computing Surveys, 33(3), 374–425.CrossRefGoogle Scholar
  11. de Weerdt, M., & Clement, B. (2009). Introduction to planning in multiagent systems. Multiagent and Grid Systems, 5, 345–355.CrossRefGoogle Scholar
  12. Decker, K., & Lesser, V. (1994). Designing a family of coordination algorithms. In Proceedings of DAI (pp. 65–84).Google Scholar
  13. Desaraju, V. R., & How, J. P. (2012). Decentralized path planning for multi-agent teams with complex constraints. Autonomous Robots, 32(4), 385–403.CrossRefGoogle Scholar
  14. Diankov, R. (2010). Automated construction of robotic manipulation programs. Ph.D. thesis, Carnegie Mellon University, Robotics Institute.Google Scholar
  15. Dovier, A., Formisano, A., & Pontelli, E. (2009). An empirical study of constraint logic programming and answer set programming solutions of combinatorial problems. Journal of Experimental and Theoretical Artificial Intelligence, 21(2), 79–121.CrossRefzbMATHGoogle Scholar
  16. Duff, D. J., Erdem, E., & Patoglu, V. (2013). Integration of 3D object recognition and planning for robotic manipulation: A preliminary report. In Proceedings ICLP 2013 workshop on knowledge representation and reasoning in robotics.Google Scholar
  17. Ehtamo, H., Hamalainen, R. P., Heiskanen, P., Teich, J., Verkama, M., & Zionts, S. (1999). Generating pareto solutions in a two-party setting: Constraint proposal methods. Management Science, 45(12), 1697–1709.CrossRefzbMATHGoogle Scholar
  18. Eiter, T., Ianni, G., Schindlauer, R., & Tompits, H. (2005). A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In Proceedings of IJCAI (pp. 90–96).Google Scholar
  19. Erdem, E., Aker, E., & Patoglu, V. (2012a). Answer set programming for collaborative housekeeping robotics: Representation, reasoning, and execution. Intelligent Service Robotics, 5(4), 275–291.CrossRefGoogle Scholar
  20. Erdem, E., Gelfond, M., & Leone, N. (2016a). Applications of answer set programming. AI Magazine, 37(3), 53–68.CrossRefGoogle Scholar
  21. Erdem, E., Haspalamutgil, K., Palaz, C., Patoglu, V., & Uras, T. (2011). Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation. In Proceedings of ICRA.Google Scholar
  22. Erdem, E., Haspalamutgil, K., Patoglu, V., & Uras, T. (2012b). Causality-based planning and diagnostic reasoning for cognitive factories. In Proceedings of IEEE international conference on emerging technologies and factory automation (ETFA).Google Scholar
  23. Erdem, E., Patoglu, V., Saribatur, Z. G., Schüller, P., & Uras, T. (2013). Finding optimal plans for multiple teams of robots through a mediator: A logic-based approach. Theory and Practice of Logic Programming, 13(4–5), 831–846.MathSciNetCrossRefzbMATHGoogle Scholar
  24. Erdem, E., Patoglu, V., & Schüller, P. (2016b). A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks. AI Communications, 29(2), 319–349.MathSciNetCrossRefGoogle Scholar
  25. Erol, K., Nau, D. S., & Subrahmanian, V. S. (1995). Complexity, decidability and undecidability results for domain-independent planning. Artificial Intelligence, 76(1–2), 75–88.MathSciNetCrossRefzbMATHGoogle Scholar
  26. Finger, J. (1986). Exploiting constraints in design synthesis. Ph.D. thesis, Stanford University.Google Scholar
  27. Foulser, D., Li, M., & Yang, Q. (1992). Theory and algorithms for plan merging. Artificial Intelligence, 57, 143–182.MathSciNetCrossRefzbMATHGoogle Scholar
  28. Gaschler, A., Petrick, R. P. A., Giuliani, M., Rickert, M., & Knoll, A. (2013). KVP: A knowledge of volumes approach to robot task planning. In Proceedings of IROS (pp. 202–208).Google Scholar
  29. Gaston, M. E., & desJardins, M. (2008). The effect of network structure on dynamic team formation in multi-agent systems. Computational Intelligence, 24(2), 122–157.MathSciNetCrossRefGoogle Scholar
  30. Gebser, M., Kaufmann, B., Neumann, A., & Schaub, T. (2007). clasp: A conflict-driven answer set solver. In Proceedings of LPNMR.Google Scholar
  31. Gelfond, M., & Lifschitz, V. (1991). Classical negation in logic programs and disjunctive databases. New Generation Computing, 9, 365–385.CrossRefzbMATHGoogle Scholar
  32. Georgeff, M. P. (1988). Communication and interaction in multi-agent planning. In Proceedings of DAI (pp. 200–204).Google Scholar
  33. Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., & Turner, H. (2004). Nonmonotonic causal theories. Artificial Intelligence, 153, 49–104.MathSciNetCrossRefzbMATHGoogle Scholar
  34. Gombolay, M. C., Wilcox, R. J., & Shah, J. A. (2013). Fast scheduling of multi-robot teams with temporospatial constraints. In Proceedings of RSS.Google Scholar
  35. Gravot, F., Cambon, S., & Alami, R. (2005). Robotics research the eleventh international symposium. In Springer tracts in advanced robotics, vol. 15, chap. aSyMov: A Planner That Deals with Intricate Symbolic and Geometric Problems (pp. 100–110). Springer.Google Scholar
  36. Hauser, K., & Latombe, J. C. (2009). Integrating task and PRM motion planning: Dealing with many infeasible motion planning queries. In Workshop on bridging the gap between task and motion planning at ICAPS.Google Scholar
  37. Havur, G., Haspalamutgil, K., Palaz, C., Erdem, E., & Patoglu, V. (2013). A case study on the tower of hanoi challenge: Representation, reasoning and execution. In Proceedings of ICRA.Google Scholar
  38. Havur, G., Ozbilgin, G., Erdem, E., & Patoglu, V. (2014). Geometric rearrangement of multiple movable objects on cluttered surfaces: A hybrid reasoning approach. In Proceedings of ICRA (pp. 445–452).Google Scholar
  39. Hertle, A., Dornhege, C., Keller, T., & Nebel, B. (2012). Planning with semantic attachments: An object-oriented view. In Proceedings of ECAI (pp. 402–407).Google Scholar
  40. Hooker, J. (2005). A hybrid method for the planning and scheduling. Constraints, 10(4), 385–401.MathSciNetCrossRefzbMATHGoogle Scholar
  41. Kaelbling, L. P., & Lozano-Pérez, T. (2013). Integrated task and motion planning in belief space. International Journal of Robotics Research, 32(9–10), 1194–1227.CrossRefGoogle Scholar
  42. Kavraki, L., Svestka, P., Latombe, J., & Overmars, M. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.CrossRefGoogle Scholar
  43. Kuffner Jr., J. J., & Lavalle, S. M. (2000b). RRT-Connect: An efficient approach to single-query path planning. In Proceedings of ICRA (pp. 995–1001).Google Scholar
  44. Kuffner Jr., J., & LaValle, S. (2000a). RRT-connect: An efficient approach to single-query path planning. In Proceedings of ICRA (pp. 995–1001).Google Scholar
  45. Lagriffoul, F., Dimitrov, D., Bidot, J., Saffiotti, A., & Karlsson, L. (2014). Efficiently combining task and motion planning using geometric constraints. International Journal of Robotics Research, 33(14), 1726–1747.CrossRefGoogle Scholar
  46. Latombe, J. C. (1991). Robot motion planning. Dordrecht: Kluwer Academic.CrossRefzbMATHGoogle Scholar
  47. LaValle, S., & Hutchinson, S. (1998). Optimal motion planning for multiple robots having independent goals. IEEE Transactions on Robotics and Automation, 14(6), 912–925.CrossRefGoogle Scholar
  48. Lin, S. H. (2011). Coordinating time-constrained multi-agent resource sharing with fault detection. In Proceedings of IEEM (pp. 1000–1004).Google Scholar
  49. Luo, L., Chakraborty, N., & Sycara, K. (2013). Distributed algorithm design for multi-robot task assignment with deadlines for tasks. In Proceedings of ICRA.Google Scholar
  50. Maliah, S., Shani, G., & Stern, R. (2014). Privacy preserving landmark detection. In Proceedings of ECAI (pp. 597–602).Google Scholar
  51. McCarthy, J. (1980). Circumscription—a form of non-monotonic reasoning. Artificial Intelligence, 13(27–39), 171–172.MathSciNetCrossRefzbMATHGoogle Scholar
  52. McCarthy, J., & Hayes, P. (1969). Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer & D. Michie (Eds.), Machine intelligence (Vol. 4, pp. 463–502). Edinburgh: Edinburgh University Press.Google Scholar
  53. Nair, R., Tambe, M., & Marsella, S. (2002). Team formation for reformation in multiagent domains like robocuprescue. In Proceedings of RoboCup (pp. 150–161).Google Scholar
  54. Nogueira, M., Balduccini, M., Gelfond, M., Watson, R., & Barry, M. (2001). An A-Prolog decision support system for the space shuttle. In Proceedings of PADL (pp. 169–183).Google Scholar
  55. Plaku, E., & Hager, G. D. (2010). Sampling-based motion and symbolic action planning with geometric and differential constraints. In Proceedings of ICRA (pp. 5002–5008).Google Scholar
  56. Ricca, F., Grasso, G., Alviano, M., Manna, M., Lio, V., Iiritano, S., et al. (2012). Team-building with answer set programming in the Gioia-Tauro seaport. Theory and Practice of Logic Programming, 12(3), 361–381.MathSciNetCrossRefzbMATHGoogle Scholar
  57. Saribatur, Z. G., Erdem, E., & Patoglu, V. (2014). Cognitive factories with multiple teams of heterogeneous robots: Hybrid reasoning for optimal feasible global plans. In Proceedings of IROS (pp. 2923–2930).Google Scholar
  58. Shoham, Y., & Tennenholtz, M. (1995). On social laws for artificial agent societies:Off-line design. Artificial Intelligence, 73, 231–252.CrossRefGoogle Scholar
  59. Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S., & Abbeel, P. (2014). Combined task and motion planning through an extensible planner-independent interface layer. In Proceedings of ICRA (pp. 639–646).Google Scholar
  60. Stuart, C. (1985). An implementation of a multi-agent plan synchronizer. In Proceedings of IJCAI (pp. 1031–1033).Google Scholar
  61. Sucan, I. A., Moll, M., & Kavraki, L. E. (2012). The open motion planning library. IEEE Robotics and Automation Magazine, 19(4), 72–82.CrossRefGoogle Scholar
  62. Sycara, K. P., Roth, S. P., Sadeh, N. M., & Fox, M. S. (1991). Resource allocation in distributed factory scheduling. IEEE Expert, 6(1), 29–40.CrossRefGoogle Scholar
  63. Tan, W., & Khoshnevis, B. (2000). Integration of process planning and scheduling—a review. Journal of Intelligent Manufacturing, 11(1), 51–63.CrossRefGoogle Scholar
  64. ter Mors, A., Valk, J., & Witteveen, C. (2004). Coordinating autonomous planners. In Proceedings of IC-AI (pp. 795–801).Google Scholar
  65. Tiihonen, J., Soininen, T., & Sulonen, R. (2003). A practical tool for mass-customising configurable products. In Proceedings of the international conference on engineering design (pp. 1290–1299).Google Scholar
  66. Torreño, A., Onaindia, E., & Sapena, O. (2012). An approach to multi-agent planning with incomplete information. In Proceedings of ECAI (pp. 762–767).Google Scholar
  67. Torreño, A., Sapena, O., & Onaindia, E. (2015). Global heuristics for distributed cooperative multi-agent planning. In Proceedings of ICAPS (pp. 225–233).Google Scholar
  68. Trejo, R., Galloway, J., Sachar, C., Kreinovich, V., Baral, C., & Tuan, L. C. (2001). From planning to searching for the shortest plan: An optimal transition. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(6), 827–837.zbMATHGoogle Scholar
  69. Turpin, M., Michael, N., & Kumar, V. (2013). Concurrent assignment and planning of trajectories for large teams of interchangeable robots. In Proceedings of ICRA (pp. 842–848).Google Scholar
  70. Velagapudi, P., Sycara, K. P., & Scerri, P. (2010). Decentralized prioritized planning in large multirobot teams. In Proceedings of IROS (pp. 4603–4609).Google Scholar
  71. Weyhrauch, R. W. (1978). Prolegomena to a theory of formal reasoning. Tech. rep.: Stanford University.Google Scholar
  72. Wolfe, J., Marthi, B., & Russell, S. (2010). Combined task and motion planning for mobile manipulation. In Proceedings of ICAPS (pp. 254–258).Google Scholar
  73. Yang, Q., Nau, D. S., & Hendler, J. (1992). Merging separately generated plans with restricted interactions. Computational Intelligence, 8, 648–676.CrossRefGoogle Scholar
  74. Zaeh, M., Beetz, M., Shea, K., Reinhart, G., Bender, K., Lau, C., Ostgathe, M., Vogl, W., Wiesbeck, M., Engelhard, M., Ertelt, C., Rühr, T., Friedrich, M., & Herle, S. (2009). The cognitive factory. In Changeable and reconf. manufacturing systems (pp. 355–371).Google Scholar

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Authors and Affiliations

  1. 1.Institute of Logic and ComputationTU WienViennaAustria
  2. 2.Faculty of Engineering and Natural SciencesSabancı UniversityIstanbulTurkey

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