Skip to main content
Log in

RHIZOME ARCHITECTURE: An Adaptive Neurobehavioral Control Architecture for Cognitive Mobile Robots—Application in a Vision-Based Indoor Robot Navigation Context

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

In this paper, a control architecture called Robotic Hybrid Indoor-Zone Operational ModulE (RHIZOME) is proposed as a new control paradigm capable of easy adaptation to different scenarios where a robot is able to interact with its environment and other cognitive agents while coping with possible unexpected situations. It creates a synergy of different state-of-the-art control paradigms by merging them into a neural structure, which follows a perception-action mechanism that constantly evolves because of the dynamic interaction of the robot with its environment. The RHIZOME architecture was tested on the NAO robot humanoid in an indoor vision-based navigation context. The proposed architecture was conceived, built and implemented through three different scenarios under which, three interdependent architectures emerged, each responding to different scenario constraints (deterministic and stochastic). Thanks to the generic composition, it is possible to develop it further with respect to robustness and completeness by simply adding new modules without modifying the already in-built components. Hence, it can be extended to perform other cognitive tasks. Experimental results obtained from its physical implementation show the feasibility, genericity and adaptability of the architecture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41

Similar content being viewed by others

References

  1. Tutsoy O, Gongor F, Barkana DE, Kose H (2017) An emotion analysis algorithm and implementation to NAO humanoid robot. In: International conference on technology, engineering and science (IConTES), pp 316–330

  2. Mataric MJ (1992) Behavior-based control: Main properties and implications. In: Proceedings, IEEE international conference on robotics and automation, workshop on architectures for intelligent control systems, pp 46–54. Citeseer

  3. Alami R, Chatila R, Fleury S, Ghallab M, Ingrand F (1998) An architecture for autonomy. Int J Robot Res 17(4):315–337

    Article  Google Scholar 

  4. Nakhaeinia D, Tang SH, Noor SBM, Motlagh O (2011) A review of control architectures for autonomous navigation of mobile robots. Int J Phys Sci 6(2):169–174

    Google Scholar 

  5. Brooks RA (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2(1):14–23

    Article  Google Scholar 

  6. Matarić MJ, Michaud F (2008) Behavior-based systems. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Cham, pp 891–909

    Chapter  Google Scholar 

  7. Schwartz JT, Sharir M (1983) On the “piano movers” problem. II. General techniques for computing topological properties of real algebraic manifolds. Adv Appl Math 4(3):298–351

    Article  MathSciNet  MATH  Google Scholar 

  8. Chatila R, Laumond J-P (1985) Position referencing and consistent world modeling for mobile robots. In: 1985 IEEE international conference on robotics and automation. Proceedings, vol 2, pp 138–145. IEEE

  9. Takahashi O, Schilling RJ (1989) Motion planning in a plane using generalized Voronoi diagrams. IEEE Trans Robot Autom 5(2):143–150

    Article  Google Scholar 

  10. Latombe J-C, Lazanas A, Shekhar S (1991) Robot motion planning with uncertainty in control and sensing. Artif Intell 52(1):1–47

    Article  MathSciNet  MATH  Google Scholar 

  11. Kortenkamp D, Simmons R, Brugali D (2016) Robotic systems architectures and programming. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Berlin, pp 283–306

    Chapter  Google Scholar 

  12. Michaud F, Nicolescu M (2016) Behavior-based systems. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Cham, pp 307–328

    Chapter  Google Scholar 

  13. Nilsson NJ (1984) Shakey the robot. Technical report, DTIC document

  14. McGann C, Py F, Rajan K, Thomas H, Henthorn R, McEwen R (2008) A deliberative architecture for AUV control. In: IEEE international conference on robotics and automation, 2008. ICRA 2008. IEEE, pp 1049–1054

  15. Arkin RC (1989) Towards the unification of navigational planning and reactive control. Georgia Institute of Technology, Atlanta

    Google Scholar 

  16. Braitenberg V (1986) Vehicles: experiments in synthetic psychology. MIT Press, Cambridge

    Google Scholar 

  17. Vogel J, Haddadin S, Jarosiewicz B, Simeral JD, Bacher D, Hochberg LR, Donoghue JP, van der Smagt P (2015) An assistive decision-and-control architecture for force-sensitive hand-arm systems driven by human–machine interfaces. Int J Robot Res 34(6):763–780

    Article  Google Scholar 

  18. Yavuz H, Bradshaw A (2002) A new conceptual approach to the design of hybrid control architecture for autonomous mobile robots. J Intell Robot Syst 34(1):1–26

    Article  MATH  Google Scholar 

  19. Qureshi F, Terzopoulos D, Gillett R (2004) The cognitive controller: a hybrid, deliberative/reactive control architecture for autonomous robots. In: Innovations in applied artificial intelligence. Springer, Berlin, pp 1102–1111

  20. Davies T, Jnifene A (2008) Path planning and trajectory control of collaborative mobile robots using hybrid control architecture. J Syst Cybern Inform 6(4):42–48

    Google Scholar 

  21. González JC, Pulido JC, Fernández F (2017) A three-layer planning architecture for the autonomous control of rehabilitation therapies based on social robots. Cognit Syst Res 43:232–249

    Article  Google Scholar 

  22. Mazzei D, Cominelli L, Lazzeri N, Zaraki A, De Rossi D (2014) I-clips brain: a hybrid cognitive system for social robots. In: Conference on biomimetic and biohybrid systems. Springer, Berlin, pp 213–224

  23. Vernon D, Hofsten C, Fadiga L (2010) The iCub cognitive architecture, pp 121–153

  24. Rosenblatt JK (1997) Damn: a distributed architecture for mobile navigation. J Exp Theor Artif Intell 9(2–3):339–360

    Article  Google Scholar 

  25. Arkin RC (1987) Motor schema based navigation for a mobile robot: an approach to programming by behavior. In: 1987 IEEE international conference on robotics and automation. Proceedings, vol 4. IEEE, pp 264–271

  26. Payton DW, Keirsey D, Kimble DM, Krozel J, Rosenblatt JK (1992) Do whatever works: a robust approach to fault-tolerant autonomous control. Appl Intell 2(3):225–250

    Article  Google Scholar 

  27. Maes P (1989) The dynamics of action selection. Artificial Intelligence Laboratory, Vrije Universiteit Brussel, Brussels

    MATH  Google Scholar 

  28. Maes P (1990) Situated agents can have goals. Robot Auton Syst 6(1):49–70

    Article  Google Scholar 

  29. Saffiotti A (1997) The uses of fuzzy logic in autonomous robot navigation. Soft Comput 1(4):180–197

    Article  Google Scholar 

  30. Michaud F (1997) Selecting behaviors using fuzzy logic. In: Proceedings of the sixth IEEE international conference on fuzzy systems, 1997, vol 1. IEEE, pp 585–592

  31. Albus JS (1991) Outline for a theory of intelligence. IEEE Trans Syst Man Cybern 21(3):473–509

    Article  MathSciNet  Google Scholar 

  32. Burnod Y (1990) An adaptive neural network: the cerebral cortex. Masson editeur, Paris

    Google Scholar 

  33. Hecht-Nielsen R (1987) Counterpropagation networks. Appl Opt 26(23):4979–4984

    Article  Google Scholar 

  34. Edelman GM (1987) Neural Darwinism: the theory of neuronal group selection. Basic Books, New York

    Google Scholar 

  35. Gaussier P, Zrehen S (1995) Perac: a neural architecture to control artificial animals. Robot Auton Syst 16(2):291–320

    Article  Google Scholar 

  36. Renaudo E, Girard B, Chatila R, Khamassi M (2014) Design of a control architecture for habit learning in robots. In: Conference on biomimetic and biohybrid systems. Springer, pp 249–260

  37. Arkin RC, Balch T (1997) Aura: principles and practice in review. J Exp Theor Artif Intell 9(2–3):175–189

    Article  Google Scholar 

  38. Bonasso RP, Kortenkamp D, Miller DP, Slack M (1995) Experiences with an architecture for intelligent, reactive agents. In: Intelligent agents II agent theories, architectures, and languages. Springer, pp 187–202

  39. Sun R (2001) Duality of the mind: a bottom-up approach toward cognition. Psychology Press, Hove

    Book  Google Scholar 

  40. Sun R, Merrill E, Peterson T (2001) From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognit Sci 25(2):203–244

    Article  Google Scholar 

  41. Carpenter GA, Grossberg S, Rosen DB (1991) Fuzzy art: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4(6):759–771

    Article  Google Scholar 

  42. Ahmed S, Liwicki M, Weber M, Dengel A (2011) Improved automatic analysis of architectural floor plans. In: 2011 international conference on document analysis and recognition. IEEE, pp 864–869

  43. Krishna Kant Singh and Akansha Singh (2010) A study of image segmentation algorithms for different types of images. Int J Comput Sci 7(5):414–417

    Google Scholar 

  44. Rodríguez-Piñeiro J, Comesaña-Alfaro P, Pérez-González F, Malvido-García A (2011) A new method for perspective correction of document images. In: IS&T/SPIE electronic imaging. International Society for Optics and Photonics, pp 787410–787410

  45. Clark P, Mirmehdi M (2002) Recognising text in real scenes. Int J Doc Anal Recogn 4(4):243–257

    Article  Google Scholar 

  46. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit 13(2):111–122

    Article  MATH  Google Scholar 

  47. Weber J, Tabbone S (2012) Symbol spotting for technical documents: an efficient template-matching approach. In: ICPR, pp 669–672

  48. Bugmann G, Taylor JG, Denham M (1995) Route finding by neural nets. In: Taylor JG (ed) Neural networks. Alfred Waller Ltd, Henley-on-Thames, pp 217–230

    Google Scholar 

  49. Connolly CI, Burns JB, Weiss R (1990) Path planning using Laplace’s equation. In: 1990 IEEE international conference on robotics and automation. Proceedings. IEEE, pp 2102–2106

  50. Rojas Castro DM, Revel A, Ménard M (2015) Document image analysis by a mobile robot for autonomous indoor navigation. In: 2015 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 156–160

  51. Cummins M, Newman P (2008) Fab-map: probabilistic localization and mapping in the space of appearance. Int J Robot Res 27(6):647–665

    Article  Google Scholar 

  52. Kin Leong Ho and Paul Newman (2007) Detecting loop closure with scene sequences. Int J Comput Vis 74(3):261–286

    Article  Google Scholar 

  53. Eade E, Drummond T (2008) Unified loop closing and recovery for real time monocular slam. BMVC 13:136

    Google Scholar 

  54. Burgess N, Recce M, O’Keefe J (1994) A model of hippocampal function. Neural Netw 7(6–7):1065–1081

    Article  MATH  Google Scholar 

  55. Brown MA, Sharp PE (1995) Simulation of spatial learning in the Morris water maze by a neural network model of the hippocampal formation and nucleus accumbens. Hippocampus 5(3):171–188

    Article  Google Scholar 

  56. Guazzelli A, Bota M, Corbacho FJ, Arbib MA (1998) Affordances. Motivations, and the world graph theory. Adapt Behav 6(3–4):435–471

    Article  Google Scholar 

  57. Redish AD, Touretzky DS (1997) Cognitive maps beyond the hippocampus. Hippocampus 7(1):15–35

    Article  Google Scholar 

  58. Filliat D, Meyer J-A et al (2002) Global localization and topological map-learning for robot navigation: from animals to animats, vol 7, pp 131–140

  59. Cartwright BA, Collett TS (1983) Landmark learning in bees. J Comp Physiol 151(4):521–543

    Article  Google Scholar 

  60. Gallistel CR (1993) Organization of learning (learning, development, and conceptual change). MIT Press, Cambridge

    Google Scholar 

  61. Judd SPD, Collett TS (1998) Multiple stored views and landmark guidance in ants. Nature 392(6677):710–714

    Article  Google Scholar 

  62. Gaussier P, Joulain C, Banquet J-P, Leprêtre S, Revel A (2000) The visual homing problem: an example of robotics/biology cross fertilization. Robot Auton Syst 30(1):155–180

    Article  Google Scholar 

  63. Rojas-Castro DM, Revel A, Ménard M (2016) Robotic and document analysis cross-fertilization: improving place cells based robot navigation. In: 2016 14th international conference on control, automation, robotics and vision (ICARCV). IEEE, pp 1–6

  64. Göngör F, Tutsoy Ö (2019) Design and implementation of a facial character analysis algorithm for humanoid robots. Robotica 37:1–17

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dalia Marcela Rojas-Castro.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rojas-Castro, D.M., Revel, A. & Menard, M. RHIZOME ARCHITECTURE: An Adaptive Neurobehavioral Control Architecture for Cognitive Mobile Robots—Application in a Vision-Based Indoor Robot Navigation Context. Int J of Soc Robotics 12, 659–688 (2020). https://doi.org/10.1007/s12369-019-00602-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-019-00602-2

Keywords

Navigation