Skip to main content
Log in

A neuroplasticity-inspired neural circuit for acoustic navigation with obstacle avoidance that learns smooth motion paths

Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Acoustic spatial navigation for mobile robots is relevant in the absence of reliable visual information about the target that must be localised. Reactive robot navigation in such goal-directed phonotaxis tasks requires generating smooth motion paths towards the acoustic target while simultaneously avoiding obstacles. We have reported earlier on a neural circuit for acoustic navigation which learned stable robot motion paths for a simulated mobile robot. However, in complex environments, the learned motion paths were not smooth. Here, we extend our earlier architecture, by adding a path-smoothing behaviour, to generate smooth motion paths for a simulated mobile robot. This allows the robot to learn to smoothly navigate towards a virtual sound source while avoiding randomly placed obstacles in the environment. We demonstrate through five independent learning trials in simulation that the proposed extension learns motion paths that are not only smooth but also relatively shorter as compared to those generated without learning as well as by our earlier 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.

Institutional subscriptions

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

Notes

  1. Modified from https://en.wikipedia.org/wiki/Nonsynaptic_plasticity#/media/File:Neurons_big1.jpg.

References

  1. Alves S, Rosario J, Ferasoli Filho H, Rincon L, Yamasaki R (2011) Conceptual bases of robot navigation modeling, control and applications. In: Barrera A (ed) Advances in robot navigation. InTech, London. https://doi.org/10.5772/20955

    Google Scholar 

  2. Andersson S, Shah V, Handzel A, Krishnaprasad P (2004) Robot phonotaxis with dynamic sound source localization. In: Proceedings of IEEE international conference on robotics and automation, 2004. ICRA ’04, vol 5, pp 4833–4838. https://doi.org/10.1109/ROBOT.2004.1302483

  3. Arkin R (1998) Behavior-based robotics. MIT Press, Cambridge

    Google Scholar 

  4. Bicho E, Mallet P, Schöner G (2000) Target representation on an autonomous vehicle with low-level sensors. Int J Robot Res 19(5):424–447. https://doi.org/10.1177/02783640022066950

    Article  Google Scholar 

  5. Braitenberg V (1984) Vehicles: experiments in synthetic psychology. MIT Press, Cambridge

    Google Scholar 

  6. Brooks R (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2(1):14–23. https://doi.org/10.1109/JRA.1986.1087032

    Article  Google Scholar 

  7. Choi JW, Curry R, Elkaim G (2008) Path planning based on Bézier curve for autonomous ground vehicles. In: Advances in electrical and electronics engineering—IAENG special edition of the world congress on engineering and computer science 2008, pp 158–166 . https://doi.org/10.1109/WCECS.2008.27

  8. Christensen-Dalsgaard J, Manley G (2005) Directionality of the lizard ear. J Exp Biol 208(6):1209–1217

    Article  Google Scholar 

  9. Dasgupta S, Wörgötter F, Manoonpong P (2014) Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control. Front Neural Circuits 8:126. https://doi.org/10.3389/fncir.2014.00126

    Article  Google Scholar 

  10. Dudek G, Jenkin M (2010) Computational principles of mobile robotics, 2nd edn. Cambridge University Press, New York

    Book  MATH  Google Scholar 

  11. Farin G (2001) Curves and surfaces for CAGD: a practical guide. The Morgan Kaufmann series in computer graphics. Elsevier, Amsterdam

    Google Scholar 

  12. Fletcher N, Thwaites S (1979) Physical models for the analysis of acoustical systems in biology. Q Rev Biophys 12(1):25–65

    Article  Google Scholar 

  13. Fraichard T, Scheuer A (2004) From Reeds and Shepp’s to continuous-curvature paths. IEEE Trans Robot 20(6):1025–1035. https://doi.org/10.1109/TRO.2004.833789

    Article  Google Scholar 

  14. Franz M, Mallot H (2000) Biomimetic robot navigation. Robot Auton Syst 30(1):133–153. https://doi.org/10.1016/S0921-8890(99)00069-X

    Article  Google Scholar 

  15. Hebb D (2005) The organization of behavior: a neuropsychological theory. Psychology Press, London

    Google Scholar 

  16. Huang J, Supaongprapa T, Terakura I, Wang F, Ohnishi N, Sugie N (1999) A model-based sound localization system and its application to robot navigation. Robot Auton Syst 27(4):199–209. https://doi.org/10.1016/S0921-8890(99)00002-0

    Article  Google Scholar 

  17. Hwang BY, Park SH, Han JH, Kim MG, Lee JM (2014) Sound-source tracking and obstacle avoidance system for the mobile robot. Springer, Cham, pp 181–192. https://doi.org/10.1007/978-3-319-05711-8_19

    Google Scholar 

  18. Kanayama Y, Hartman B (1997) Smooth local-path planning for autonomous vehicles. Int J Robot Res 16(3):263–284. https://doi.org/10.1177/027836499701600301

    Article  Google Scholar 

  19. Klopf A (1988) A neuronal model of classical conditioning. Psychobiology 16(2):85–125

    Google Scholar 

  20. Komoriya K, Tanie K (1989) Trajectory design and control of a wheel-type mobile robot using B-spline curve. In: Proceedings of IEEE/RSJ international workshop on intelligent robots and systems ’89. The autonomous mobile robots and its applications. IROS ’89, pp 398–405. https://doi.org/10.1109/IROS.1989.637937

  21. Kosko B (1986) Differential Hebbian learning. AIP Conf Proc 151(1):277–282

    Article  Google Scholar 

  22. Lamiraux F, Lammond JP (2001) Smooth motion planning for car-like vehicles. IEEE Trans Robot Autom 17(4):498–501. https://doi.org/10.1109/70.954762

    Article  Google Scholar 

  23. LaValle S (2006) Planning algorithms. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511546877

    Book  MATH  Google Scholar 

  24. Magid E, Keren D, Rivlin E, Yavneh I (2006) Spline-based robot navigation. In: 2006 IEEE/RSJ international conference on intelligent robots and systems, pp 2296–2301. https://doi.org/10.1109/IROS.2006.282635

  25. Manoonpong P, Kolodziejski C, Wörgötter F, Morimoto J (2013) Combining correlation-based and reward-based learning in neural control for policy improvement. Adv Complex Syst 16(02n03):1350,015. https://doi.org/10.1142/S021952591350015X

    Article  MathSciNet  Google Scholar 

  26. Manoonpong P, Wörgötter F (2009) Neural information processing. In: Proceedings of the 16th international conference, ICONIP 2009, part II, chap. adaptive sensor-driven neural control for learning in walking machines, Bangkok, Thailand, 1–5 December 2009. Springer, Berlin, pp 47–55

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

    Google Scholar 

  28. Ön S, Yazici A (2011) A comparative study of smooth path planning for a mobile robot considering kinematic constraints. In: 2011 international symposium on innovations in intelligent systems and applications, pp 565–569. https://doi.org/10.1109/INISTA.2011.5946138

  29. Porr B, Wörgötter F (2006) Strongly improved stability and faster convergence of temporal sequence learning by utilising input correlations only. Neural Comput 18(6):1380–1412

    Article  MATH  Google Scholar 

  30. Porr B, Wörgötter F (2007) Fast heterosynaptic learning in a robot food retrieval task inspired by the limbic system. Biosystems 89(1–3):294–299 (Selected Papers presented at the 6th International Workshop on Neural Coding)

    Article  Google Scholar 

  31. Purves D, Augustine G, Fitzpatrick D, Hall W, LaMantia A, White L (2012) Synaptic plasticity. Neuroscience, 5th edn. Sinauer Associates, Sunderland, pp 163–182

    Google Scholar 

  32. Ravankar A, Ravankar A, Kobayashi Y, Emaru T (2016) Path smoothing extension for various robot path planners. In: 2016 16th international conference on control, automation and systems (ICCAS), pp 263–268. https://doi.org/10.1109/ICCAS.2016.7832330

  33. Shaikh D, Hallam J, Christensen-Dalsgaard J (2010) Modifying directionality through auditory system scaling in a robotic lizard. Springer, Berlin, pp 82–92. https://doi.org/10.1007/978-3-642-15193-4_8

  34. Shaikh D, Hallam J, Christensen-Dalsgaard J (2016) From ear to there: a review of biorobotic models of auditory processing in lizards. Biol Cybern 110(4):303–317. https://doi.org/10.1007/s00422-016-0701-y

    Article  MATH  Google Scholar 

  35. Shaikh D, Manoonpong P (2017) A neural circuit for acoustic navigation combining heterosynaptic and non-synaptic plasticity that learns stable trajectories. Springer, Cham, pp 544–555. https://doi.org/10.1007/978-3-319-65172-9_46

    Google Scholar 

  36. Takahashi A, Hongo T, Ninomiya Y, Sugimoto G (1989) Local path planning and motion control for AGV in positioning. In: Proceedings of IEEE/RSJ international workshop on intelligent robots and systems ’89. IROS ’89. The autonomous mobile robots and its applications, pp 392–397. https://doi.org/10.1109/IROS.1989.637936

  37. Wever E (1978) The reptile ear: its structure and function. Princeton University Press, Princeton

    Google Scholar 

  38. Zeno P, Patel S, Sobh T (2016) Review of neurobiologically based mobile robot navigation system research performed since 2000. J Robot. https://doi.org/10.1155/2016/8637251

  39. Zhang W, Linden D (2003) The other side of the engram: experience-driven changes in neuronal intrinsic excitability. Nat Rev Neurosci 4(11):885–900. https://doi.org/10.1038/nrn1248

    Article  Google Scholar 

  40. Zu L, Yang P, Zhang Y, Chen L, Sun H (2009) Study on navigation system of mobile robot based on auditory localization. In: 2009 IEEE international conference on robotics and biomimetics (ROBIO), pp 321–326. https://doi.org/10.1109/ROBIO.2009.5420665

  41. Zuojun L, Guangyao L, Peng Y, Feng L, Chu C (2012) Behavior based rescue robot audio navigation and obstacle avoidance. In: Proceedings of the 31st Chinese control conference, pp 4847–4851

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Danish Shaikh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

This research was supported with a Grant for the SMOOTH Project (Project Number 6158-00009B) by Innovation Fund Denmark.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shaikh, D., Manoonpong, P. A neuroplasticity-inspired neural circuit for acoustic navigation with obstacle avoidance that learns smooth motion paths. Neural Comput & Applic 31, 1765–1781 (2019). https://doi.org/10.1007/s00521-018-3845-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3845-y

Keywords

Navigation