Advertisement

A Neural Circuit for Acoustic Navigation Combining Heterosynaptic and Non-synaptic Plasticity That Learns Stable Trajectories

  • Danish Shaikh
  • Poramate Manoonpong
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 744)

Abstract

Reactive spatial robot navigation in goal-directed tasks such as phonotaxis requires generating consistent and stable trajectories towards an acoustic target while avoiding obstacles. High-level goal-directed steering behaviour can steer a robot towards the target by mapping sound direction information to appropriate wheel velocities. However, low-level obstacle avoidance behaviour based on distance sensors may significantly alter wheel velocities and temporarily direct the robot away from the sound source, creating conflict between the two behaviours. How can such a conflict in reactive controllers be resolved in a manner that generates consistent and stable robot trajectories? We propose a neural circuit that minimises this conflict by learning sensorimotor mappings as neuronal transfer functions between the perceived sound direction and wheel velocities of a simulated non-holonomic mobile robot. These mappings constitute the high-level goal-directed steering behaviour. Sound direction information is obtained from a model of the lizard peripheral auditory system. The parameters of the transfer functions are learned via an online unsupervised correlation learning algorithm through interaction with obstacles in the form of low-level obstacle avoidance behaviour in the environment. The simulated robot is able to navigate towards a virtual sound source placed 3 m away that continuously emits a tone of frequency 2.2 kHz, while avoiding randomly placed obstacles in the environment. We demonstrate through two independent trials in simulation that in both cases the neural circuit learns consistent and stable trajectories as compared to navigation without learning.

Keywords

Behaviour-based robotics Reactive navigation Phonotaxis Lizard peripheral auditory system Synaptic plasticity Correlation-based learning 

Notes

Acknowledgements

This research was supported with a grant for the SMOOTH project (project number 6158-00009B) by Innovation Fund Denmark.

References

  1. 1.
    Alves, S., Rosario, J., Ferasoli Filho, H., Rincon, L., Yamasaki, R.: Conceptual bases of robot navigation modeling, control and applications. In: Barrera, A. (ed.) Advances in Robot Navigation. InTech (2011)Google Scholar
  2. 2.
    Andersson, S., Shah, V., Handzel, A., Krishnaprasad, P.: Robot phonotaxis with dynamic sound source localization. In: Proceedings 2004 IEEE International Conference on Robotics and Automation, ICRA 2004, vol. 5, pp. 4833–4838, April 2004Google Scholar
  3. 3.
    Arkin, R.: Behavior-Based Robotics. MIT Press, Cambridge (1998)Google Scholar
  4. 4.
    Bicho, E., Mallet, P., Schner, G.: Target representation on an autonomous vehicle with low-level sensors. Int. J. Robo. Res. 19(5), 424–447 (2000)CrossRefGoogle Scholar
  5. 5.
    Braitenberg, V.: Vehicles: Experiments in Synthetic Psychology. MIT Press, Cambridge (1984). Bradford BooksGoogle Scholar
  6. 6.
    Brooks, R.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2(1), 14–23 (1986)CrossRefGoogle Scholar
  7. 7.
    Christensen-Dalsgaard, J., Manley, G.: Directionality of the lizard ear. J. Exp. Biol. 208(6), 1209–1217 (2005)CrossRefGoogle Scholar
  8. 8.
    Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics, 2nd edn. Cambridge University Press, New York (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Fletcher, N., Thwaites, S.: Physical models for the analysis of acoustical systems in biology. Q. Rev. Biophys. 12(1), 25–65 (1979)CrossRefGoogle Scholar
  10. 10.
    Hebb, D.: The Organization of Behavior: A Neuropsychological Theory. Psychology Press, Abingdon (2005)Google Scholar
  11. 11.
    Huang, J., Supaongprapa, T., Terakura, I., Wang, F., Ohnishi, N., Sugie, N.: A model-based sound localization system and its application to robot navigation. Robot. Auton. Syst. 27(4), 199–209 (1999)CrossRefGoogle Scholar
  12. 12.
    Hwang, B.-Y., Park, S.-H., Han, J.-H., Kim, M.-G., Lee, J.-M.: Sound-source tracking and obstacle avoidance system for the mobile robot. In: Tutsch, R., Cho, Y.-J., Wang, W.-C., Cho, H. (eds.) Progress in Optomechatronic Technologies. LNEE, vol. 306, pp. 181–192. Springer, Cham (2014). doi: 10.1007/978-3-319-05711-8_19 CrossRefGoogle Scholar
  13. 13.
    Janis, A., Bade, A.: Path planning algorithm in complex environment: a survey. Trans. Sci. Technol. 3(1), 31–40 (2016)Google Scholar
  14. 14.
    Klopf, A.: A neuronal model of classical conditioning. Psychobiology 16(2), 85–125 (1988)Google Scholar
  15. 15.
    Kosko, B.: Differential Hebbian learning. In: AIP Conference Proceedings, vol. 151, no. 1, pp. 277–282 (1986)Google Scholar
  16. 16.
    Manoonpong, P., Wörgötter, F.: Adaptive sensor-driven neural control for learning in walking machines. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009. LNCS, vol. 5864, pp. 47–55. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-10684-2_6 CrossRefGoogle Scholar
  17. 17.
    Nakhaeinia, D., Tang, S., Noor, S., Motlagh, O.: A review of control architectures for autonomous navigation of mobile robots. Int. J. Phys. Sci. 6(2), 169–174 (2011)Google Scholar
  18. 18.
    Porr, B., Wörgötter, F.: Fast heterosynaptic learning in a robot food retrieval task inspired by the limbic system. Biosystems 89(1–3), 294–299 (2007). (In: Selected Papers Presented at the 6th International Workshop on Neural Coding)CrossRefGoogle Scholar
  19. 19.
    Porr, B., Wörgötter, F.: Strongly improved stability and faster convergence of temporal sequence learning by utilising input correlations only. Neural Comput. 18(6), 1380–1412 (2006)CrossRefzbMATHGoogle Scholar
  20. 20.
    Purves, D., Augustine, G., Fitzpatrick, D., Hall, W., LaMantia, A., White, L.: Synaptic plasticity. In: Neuroscience, 5th edn., pp. 163–182. Sinauer Associates, Sunderland (2012)Google Scholar
  21. 21.
    Shaikh, D., Hallam, J., Christensen-Dalsgaard, J.: From “ear” to there: a review of biorobotic models of auditory processing in lizards. Biol. Cybern. 110(4), 303–317 (2016)CrossRefGoogle Scholar
  22. 22.
    Tang, S., Kamil, F., Khaksar, W., Zulkifli, N., Ahmad, S.: Robotic motion planning in unknown dynamic environments: existing approaches and challenges. In: 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 288–294, October 2015Google Scholar
  23. 23.
    Wever, E.: The Reptile Ear: Its Structure and Function. Princeton University Press, Princeton (1978)Google Scholar
  24. 24.
    Zeno, P., Patel, S., Sobh, T.: Review of neurobiologically based mobile robot navigation system research performed since 2000. J. Robot. 2016 (2016)Google Scholar
  25. 25.
    Zhang, W., Linden, D.: The other side of the engram: experience-driven changes in neuronal intrinsic excitability. Nat. Rev. Neurosci. 4(11), 885–900 (2003)CrossRefGoogle Scholar
  26. 26.
    Zu, L., Yang, P., Zhang, Y., Chen, L., Sun, H.: Study on navigation system of mobile robot based on auditory localization. In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 321–326, December 2009Google Scholar
  27. 27.
    Zuojun, L., Guangyao, L., Peng, Y., Feng, L., Chu, C.: Behavior based rescue robot audio navigation and obstacle avoidance. In: Proceedings of the 31st Chinese Control Conference, pp. 4847–4851, July 2012Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Embodied AI and Neurorobotics Laboratory, Centre for BioRobotics, Maersk Mc-Kinney Moeller InstituteUniversity of Southern DenmarkOdense MDenmark

Personalised recommendations