Skip to main content

Behavior and Path Planning for the Coalition of Cognitive Robots in Smart Relocation Tasks

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 447)

Abstract

In this paper we outline the approach of solving special type of navigation tasks for robotic systems, when a coalition of robots (agents) acts in the 2D environment, which can be modified by the actions, and share the same goal location. The latter is originally unreachable for some members of the coalition, but the common task still can be accomplished as the agents can assist each other (e.g., by modifying the environment). We call such tasks smart relocation tasks (as they cannot be solved by pure path planning methods) and study spatial and behavior interaction of robots while solving them. We use cognitive approach and introduce semiotic knowledge representation—sign world model which underlines behavioral planning methodology. Planning is viewed as a recursive search process in the hierarchical state-space induced by sings with path planning signs residing on the lowest level. Reaching this level triggers path planning which is accomplished by state-of-the-art grid-based planners focused on producing smooth paths (e.g., LIAN) and thus indirectly guarantying feasibility of that paths against agent’s dynamic constraints.

Keywords

  • Behavior planning
  • Task planning
  • Coalition
  • Path planning
  • Sign world model
  • Semiotic model
  • Knowledge representation
  • LIAN

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-31293-4_1
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   299.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-31293-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   379.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Albus, J.S.: 4D/RCS: a reference model architecture for intelligent unmanned ground vehicles. AeroSense 2002. International Society for Optics and Photonics (2002)

    Google Scholar 

  2. Yoo, J.-K., Kim, J.-H.: Gaze control-based navigation architecture with a situation-specific preference approach for humanoid robots. IEEE Trans. Mechatron. (2015)

    Google Scholar 

  3. Emelyanov, S., et al.: Multilayer cognitive architecture for UAV control. Cogn. Syst. Res. 34 (2015)

    Google Scholar 

  4. Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier (2004)

    Google Scholar 

  5. Ghallab, M., et al.: PDDL-the planning domain definition language (1998)

    Google Scholar 

  6. Fox, M., Long, D.: PDDL2. 1: an extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. (JAIR) 20, 61–124 (2003)

    MATH  Google Scholar 

  7. Karlsson, L., et al.: Combining task and path planning for a humanoid two-arm robotic system. In: TAMPRA’12: Proceedings of the 2012 ICAPS Workshop on Combining Task and Motion Planning for Real-World Applications, pp. 13–20 (2012)

    Google Scholar 

  8. Abdo, N., Kretzschmar, H., Stachniss, C.: From low-level trajectory demonstrations to symbolic actions for planning. In: TAMPRA’12: Proceedings of the 2012 ICAPS Workshop on Combining Task and Motion Planning for Real-World Applications, pp. 29–36

    Google Scholar 

  9. Laird, J.: Extending the soar cognitive architecture. In: Proceedings of the First AGI Conference, pp. 224–235 (2008)

    Google Scholar 

  10. Laird, J.E.: The Soar Cognitive Architecture. MIT Press, Cambridge (2012)

    Google Scholar 

  11. Nilsson, N.J.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann, San Francisco (1998)

    MATH  Google Scholar 

  12. Langley, P.: Learning to sense selectively in physical domains. In: Proceedings of the First International Conference on Autonomous Agents, Marina del Rey, USA, pp. 217–226 (1997)

    Google Scholar 

  13. Langley, P.: Cognitive architectures and general intelligent systems. AI Mag. 27, 33–44 (2006)

    Google Scholar 

  14. Sun, R., Bookman, L.: Computational Architectures Integrating Neural and Symbolic Processes, p. 496. Kluwer Academic Publishers, Boston (1994)

    Google Scholar 

  15. Sun, R.: The CLARION Cognitive Architecture: Extending Cognitive Modeling to Social Simulation, p. 434. Cambridge University Press, New York (2006)

    Google Scholar 

  16. Fikes, R.E., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3–4), 189–208 (1971). http://doi.org/10.1016/0004-3702(71)90010-5

  17. Blum, A.L., Frust, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90(1–2), 281–300 (1997). doi:10.1016/S0004-3702(96)00047-1

    CrossRef  MATH  Google Scholar 

  18. Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. J. Artif. Intell. Res. 14, 253–302 (2001)

    MATH  Google Scholar 

  19. Helmert, M.: The fast downward planning system. J. Artif. Intell. Res. 26, 191–246 (2006). doi:10.1613/jair.1705

    CrossRef  MATH  Google Scholar 

  20. Richter, S., Westphal, M.: The LAMA planner: guiding cost-based anytime planning with landmarks. J. Artif. Intell. Res. 39, 127–177 (2010). doi:10.1613/jair.2972

    MATH  Google Scholar 

  21. Della Penna, G., Magazzeni, D., Mercorio, F.: A universal planning system for hybrid domains. Appl. Intell. 36(4), 932–959 (2012). doi:10.1007/s10489-011-0306-z

    CrossRef  Google Scholar 

  22. Fox, M., Long, D.: Modelling mixed discrete-continuous domains for planning. J. Artif. Intell. Res. 27, 235–297 (2006)

    MATH  Google Scholar 

  23. Kovacs, D.L.: A multi-agent extension of PDDL3.1. In: Proceedings of the 3rd Workshop on the International Planning Competition (IPC), ICAPS-2012, Atibaia, Brazil, pp. 19–27, 25–29 June 2012

    Google Scholar 

  24. Harnad, S.: Symbol Grounding Problem. Physica 42, 335–346 (1990). doi:10.4249/scholarpedia.2373

    Google Scholar 

  25. Osipov, G.S., Panov, A.I., Chudova, N.V.: Behavior control as a function of consciousness. I. World model and goal setting. J. Comput. Syst. Sci. Int. 53(4), 517–529 (2014). doi:10.1134/S1064230714040121

    MathSciNet  CrossRef  MATH  Google Scholar 

  26. Ivanitsky, A.M.: Information synthesis in key parts of the cerebral cortex as the basis of subjective experience. Neurosci. Behav. Physiol. 27(4), 414–426 (1997)

    CrossRef  Google Scholar 

  27. Edelman, G.M.: Neural Darwin-ism: The Theory Of Neuronal Group Selection. Basic Books, New York (1987)

    Google Scholar 

  28. Lozano-Prez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22(10), 560–570 (1979)

    CrossRef  Google Scholar 

  29. Bhattacharya, P., Gavrilova, M.L.: Roadmap-based path planning-using the Voronoi diagram for a clearance-based shortest path. Robot. Autom. Mag. IEEE 15(2), 58–66 (2008)

    CrossRef  Google Scholar 

  30. Kallmann, M.: Navigation queries from triangular meshes. In: Boulic, R., Chrysanthou, Y., Komura, T. (eds.) MIG 2010. LNCS, vol. 6459, pp. 230–241. Springer, Heidelberg (2010)

    Google Scholar 

  31. Yap, P.: Grid-based path-finding. In: Co-hen, R., Spencer, B. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2338, pp. 44–55. Springer, Heidelberg (2002)

    Google Scholar 

  32. Wooden, D.T.: Graph-based path planning for mobile robots. PhD thesis, Georgia Institute of Technology (2006)

    Google Scholar 

  33. Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)

    CrossRef  Google Scholar 

  34. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)

    MathSciNet  CrossRef  MATH  Google Scholar 

  35. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    CrossRef  Google Scholar 

  36. Likhachev, M., Stentz, A.: R* search. In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence. AAAI Press, Menlo Park (2008)

    Google Scholar 

  37. Nash, A., Daniel, K., Koenig, S., Felner, A. 2007. Theta*: any-angle path planning on grids. In: Proceedings of the National Conference on Artificial Intelligence, vol. 22, No. 2, pp. 1177. AAAI Press, Menlo Park, Calif

    Google Scholar 

  38. Harabor, D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: AAAI-11 (2013)

    Google Scholar 

  39. Koenig, S., Likhachev, M.: D*Lite. In: Proceedings of the National Conference on Artificial Intelligence AAAI (2000)

    Google Scholar 

  40. Kuwata, Y., Karaman, S., Teo, J., Frazzoli, E., How, J.P., Fiore, G.: Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17(5), 1105–1118 (2009)

    CrossRef  Google Scholar 

  41. Kim, H., Kim, D., Shin, J.U., Kim, H., Myung, H.: Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Eng. 84, 37–44 (2014)

    CrossRef  Google Scholar 

  42. Yakovlev, K., Baskin, E., Hramoin, I.: Grid-based angle-constrained path planning. In: Proceeding of the 38th German Conference on Artificial Intelligence (KI-2015) (2015)

    Google Scholar 

  43. Standley, T.: Finding optimal solutions to cooperative pathfinding problems. In: AAAI, pp. 173–178 (2010)

    Google Scholar 

  44. Wang, K.-H.C., Botea, A.: Fast and memory-efficient multi-agent pathfinding. In: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pp. 380–387 (2008)

    Google Scholar 

  45. Silver, D.: AI Program. Wisdom 3, 99–111 (2006)

    Google Scholar 

  46. Yakovlev, K., Makarov, D., Baskin, E.: Automatic path planning for an unmanned drone with constrained flight dynamics. Sci. Tech. Inf. Process. 5 (2015)

    Google Scholar 

  47. Leontyev, A.N.: The Development of Mind. Erythros Press and Media, Kettering (2009)

    Google Scholar 

  48. Vygotsky, L.S.: Thought and Language. MIT Press (1986)

    Google Scholar 

  49. Mountcastle, V.B.: Perceptual Neuroscience. The Cerebral Cortex. Harvard University Press, Cambridge (1998)

    Google Scholar 

  50. George, D., Hawkins, J.: PLoS Comput. Biol. 5(10), 1–26 (2009)

    MathSciNet  CrossRef  Google Scholar 

  51. Osipov, G.S., Panov, A.I., Chudova, N.V.: Behavior control as a function of consciousness. II. Synthesis of a behavior plan. J. Comput. Syst. Sci. Int. 54(5) (2015)

    Google Scholar 

  52. Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)

    CrossRef  Google Scholar 

Download references

Acknowledgments

The reported study was supported by RFBR, research projects No. 14-07-31194 and No. 15-37-20893.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aleksandr I. Panov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Panov, A.I., Yakovlev, K. (2017). Behavior and Path Planning for the Coalition of Cognitive Robots in Smart Relocation Tasks. In: Kim, JH., Karray, F., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 4. Advances in Intelligent Systems and Computing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-319-31293-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31293-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31291-0

  • Online ISBN: 978-3-319-31293-4

  • eBook Packages: EngineeringEngineering (R0)