Skip to main content
SpringerLink
Log in

Brain Cognition Mechanism-Inspired Hierarchical Navigation Method for Mobile Robots

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Autonomous navigation is a fundamental problem in robotics. Traditional methods generally build point cloud map or dense feature map in perceptual space; due to lack of cognition and memory formation mechanism, traditional methods exist poor robustness and low cognitive ability. As a new navigation technology that draws inspiration from mammal’s navigation, bionic navigation method can map perceptual information into cognitive space, and have strong autonomy and environment adaptability. To improve the robot’s autonomous navigation ability, this paper proposes a cognitive map-based hierarchical navigation method. First, the mammals’ navigation-related grid cells and head direction cells are modeled to provide the robots with location cognition. And then a global path planning strategy based on cognitive map is proposed, which can anticipate one preferred global path to the target with high efficiency and short distance. Moreover, a hierarchical motion controlling method is proposed, with which the target navigation can be divided into several sub-target navigation, and the mobile robot can reach to these sub-targets with high confidence level. Finally, some experiments are implemented, the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency, and the time cost does not increase extremely with the increase of experience nodes number. The motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information, which is an effective attempt and reflects strong bionic properties.

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

Access this article

Log in via an institution

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data Availability

The data involved in this work partly are public data, which can be downloaded through public channels, and others are available from the corresponding author upon reasonable request.

References

  1. Yasuda, Y. D. V., Martins, L. E. G., & Cappabianco, F. A. M. (2021). Autonomous visual navigation for mobile robots. ACM Computing Surveys, 53, 1–34. https://doi.org/10.1145/3368961

    Article  Google Scholar 

  2. Richter, M., Sandamirskaya, Y., & Schoner, G. (2012). A robotic architecture for action selection and behavioral organization inspired by human cognition. In IEEE/RSJ international conference on intelligent robots and systems, Vilamoura-Algarve, Portugal (pp. 2457–2464). https://doi.org/10.1109/IROS.2012.6386153

  3. Niloy, A. K., Shama, A., Chakrabortty, R. K., Ryan, M. J., & Saha, D. K. (2021). Critical design and control issues of indoor autonomous mobile robots: A review. IEEE Access, 9, 35338–35370. https://doi.org/10.1109/ACCESS.2021.3062557

    Article  Google Scholar 

  4. Barry, C., & Burgess, N. (2014). Neural mechanisms of self-location. Current Biology, 24, R330–R339. https://doi.org/10.1016/j.cub.2014.02.049

    Article  Google Scholar 

  5. Larry, R. S. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82(3), 171–177. https://doi.org/10.1016/j.nlm.2004.06.005

    Article  Google Scholar 

  6. Lisman, J., Buzsáki, G., Eichenbaum, H., Nadel, L., Ranganath, C., & Redish, A. D. (2017). Viewpoints: How the hippocampus contributes to memory, navigation and cognition. Nature Neuroscience, 20(11), 1434–1447. https://doi.org/10.1038/nn.4661

    Article  Google Scholar 

  7. Buzsáki, G., & Moser, E. (2013). Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nature Neuroscience, 16(2), 130–138. https://doi.org/10.1038/nn.3304

    Article  Google Scholar 

  8. Bicanski, A., & Burgess, N. (2020). Neuronal vector coding in spatial cognition. Nature Reviews Neuroscience, 21(9), 453–470. https://doi.org/10.1038/s41583-020-0336-9

    Article  Google Scholar 

  9. Wagatsuma, H., & Yamaguchi, Y. (2007). Neural dynamics of the cognitive map in the hippocampus. Cognitive Neurodynamics, 1(2), 119–141. https://doi.org/10.1007/s11571-006-9013-6

    Article  Google Scholar 

  10. Taube, S. J., Muller, U. R., & Ranck, B. J. (1990). Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. Journal of Neuroscience, 10(2), 436–447. https://doi.org/10.1523/JNEUROSCI.10-02-00420.1990

    Article  Google Scholar 

  11. Barry, C., Hayman, R., Burgess, N., & Jeffery, K. J. (2007). Experience-dependent rescaling of entorhinal grids. Nature Neuroscience, 10(6), 682–684. https://doi.org/10.1038/nn1905

    Article  Google Scholar 

  12. Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2014). Design principles of the hippocampal cognitive map. In International Conference on Neural Information Processing Systems, Cambridge, USA (pp. 2528–2536). https://doi.org/10.5555/2969033.2969109

  13. Pfeiffer, B. E., & Fostr, D. J. (2013). Hippocampal place-cell sequences depict future paths to remembered goals. Nature, 497(7447), 74–79. https://doi.org/10.1038/nature12112

    Article  Google Scholar 

  14. Arleo, A. (2000). Spatial learning and navigation in neuro mimetic systems: Modeling the rat hippocampus. Biological Cybernetics, 83(3), 287–299. https://doi.org/10.5075/epfl-thesis-2312

    Article  Google Scholar 

  15. Giovannangeli, C., Gaussier, P., & Desilles, G. (2006). Robust mapless outdoor vision-based navigation. In IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (pp. 3293–3300). https://doi.org/10.1109/IROS.2006.282501

  16. Giovannangeli, C., & Gaussier, P. (2008). Autonomous vision-based navigation: Goal-oriented action planning by transient states prediction, cognitive map building, and sensory-motor learning. In IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (pp. 676–683). https://doi.org/10.1109/IROS.2008.4650872

  17. Erdem, U. M., & Hasselmo, M. (2012). A goal-directed spatial navigation model using forward trajectory planning based on grid cells. European Journal of Neuroscience, 35(6), 916–931. https://doi.org/10.1111/j.1460-9568.2012.08015.x

    Article  Google Scholar 

  18. Erdem, U. M., & Hasselmo, M. E. (2014). A biologically inspired hierarchical goal directed navigation model. Journal of Physiology-Paris, 108(1), 28–37. https://doi.org/10.1016/j.jphysparis.2013.07.002

    Article  Google Scholar 

  19. Milford, M., & Wyeth, G. (2010). Persistent navigation and mapping using a biologically inspired SLAM system. The International Journal of Robotics Research, 29(9), 1131–1153. https://doi.org/10.1177/0278364909340592

    Article  Google Scholar 

  20. Hirel, J., Gaussier, P., Quoy, M., Banquet, J. P., Save, E., & Poucet, B. (2013). The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation. Neural Networks, 43, 8–21. https://doi.org/10.1016/j.neunet.2013.01.023

    Article  Google Scholar 

  21. Llofriu, M., Tejera, G., Contreras, M., Pelc, T., & Weitzenfeld, A. (2015). Goal-oriented robot navigation learning using a multi-scale space representation. Neural Networks, 72, 62–74. https://doi.org/10.1016/j.neunet.2015.09.006

    Article  Google Scholar 

  22. Li, W. L., Wu, D. W., Wu, J., & Zhou, Y. (2017). A biologically inspired model based on a multi-scale spatial representation for goal-directed navigation. KSII Transactions on Internet and Information Systems, 11(3), 1477–1491. https://doi.org/10.3837/TIIS.2017.03.013

    Article  Google Scholar 

  23. Banino, A., Barry, C., Uria, B., Blundell, C., Lillicrap, T., Mirowski, P., Pritzel, A., Chadwick, M. J., Degris, T., Modayil, J., Wayne, G., Soyer, H., Viola, F., Zhang, B., Goroshin, R., Rabinowitz, N., Pascanu, R., Beattie, C., Petersen, S., Sadik, A., Gaffney, S., King, H., Kavukcuoglu, K., Hassabis, D., Hadsell, R., Kumaran, D. (2018). Vector-based navigation using grid-like representations in artificial agents. Nature, 557(7705), 429–433. https://doi.org/10.1038/s41586-018-0102-6

    Article  Google Scholar 

  24. Edvardsen, V. (2015). A passive mechanism for goal-directed navigation using grid cells. In European Conference on Artificial Life, York, England (pp. 191–198). https://doi.org/10.7551/978-0-262-33027-5-ch039

  25. Edvardsen, V. (2019). Goal-directed navigation based on path integration and decoding of grid cells in an artificial neural network. Natural Computing, 18(1), 13–27. https://doi.org/10.1007/s11047-016-9575-0

    Article  MathSciNet  Google Scholar 

  26. Scleidorovich, P., Llofriu, M., Fellous, J. M., & Weitzenfeld, A. (2020). A computational model for spatial cognition combining dorsal and ventral hippocampal place field maps: Multiscale navigation. Biological Cybernetics, 114, 18–207. https://doi.org/10.1007/s00422-019-00812-x

    Article  Google Scholar 

  27. Chai, J., Ruan, X. G., & Huang, J. (2021). NLM-HS: Navigation learning model based on a hippocampal-striatal circuit for explaining navigation mechanisms in animal brains. Brain Science, 11(6), 803. https://doi.org/10.3390/brainsci11060803

    Article  Google Scholar 

  28. Yuan, J. S., Guo, W., Hou, Z. H., Zha, F. S., Li, M. T., Sun, L. N., & Wang, P. F. (2023). Robot navigation strategy in complex environment based on episode cognition. Journal of Bionic Engineering, 20(1), 1–15. https://doi.org/10.1007/s42235-022-00265-2

    Article  Google Scholar 

  29. Zou, Q., Cong, M., Liu, D., Du, Y., & Lyu, Z. (2020). Robotic episodic cognitive learning inspired by hippocampal spatial cells. IEEE Robotics and Automation Letters, 5(4), 5573–5580. https://doi.org/10.1109/LRA.2020.3009071

    Article  Google Scholar 

  30. Krupic, J., Burgess, N., & O’Keefe, J. (2012). Neural representations of location composed of spatially periodic bands. Science, 337(6096), 853–857. https://doi.org/10.1126/science.1222403

    Article  Google Scholar 

  31. Jiang, H., & Zhang, Y. K. (2023). A spatial location representation method incorporating boundary information. Applied Sciences, 13, 7929. https://doi.org/10.3390/app13137929

    Article  Google Scholar 

Download references

Funding

This work was funded by the National Natural Science Foundation of China-Liaoning Joint Fund (Grants: U20A20197), the National Natural Science Foundation of China (Grants: 62173064), the Fundamental Research Funds for the Central Universities (Grants: N2326005).

Author information

Authors and Affiliations

  1. Faculty of Robot Science and Engineering, Northeastern University, Shenyang, 110819, China

    Qiang Zou & Chengdong Wu

  2. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China

    Ming Cong & Dong Liu

Authors
  1. Qiang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengdong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming Cong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Zou.

Ethics declarations

Conflict of interest

The authors declare there is no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zou, Q., Wu, C., Cong, M. et al. Brain Cognition Mechanism-Inspired Hierarchical Navigation Method for Mobile Robots. J Bionic Eng 21, 852–865 (2024). https://doi.org/10.1007/s42235-023-00449-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-023-00449-4

Keywords

Search

Navigation