Abstract
In this paper, we study a robust multi modal compass for a vision based navigation system. The model mimics several aspects of the head direction cells found in the postsubiculum of the rat. Idiothetic information is recalibrated according to the learning of visual stimuli associated to robust landmarks. The model is based on dynamic neural fields allowing building attractors associated to the compass direction. The novelty of the model relies in the way the decision of the sensor fusion is re-injected in the visual compass allowing a robust decision-making. Robotics experiments show the capability of the model to merge different sources of information when their predictions are coherent. When the information become incoherent because the inputs propose quite different directions, the system is able to bifurcate on one coherent solution in order to maintain the temporal coherency of its behavior.
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References
Amari, S.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics 27(2), 77–87 (1977)
Arleo, A.: Spatial learning and navigation in neuro-mimetic systems. In: Ph.D. dissertation, Universit de Paris VI (2000)
Arleo, A., Déjean, C., Allegraud, P., Khamassi, M., Zugaro, M.B., Wiener, S.I.: Optic flow stimuli update anterodorsal thalamus head direction neuronal activity in rats. Journal of Neuroscience 33, 16790–16795 (2013)
Caltabiano, D., Muscato, G., Russo, F.: Localization and self-calibration of a robot for volcano exploration. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 586–591 (2004)
Cartwright, B., Collett, T.: Landmark learning in bees 151, 521–543 (1983)
Cuperlier, N., Quoy, M., Gaussier, P.: Neurobiologically inspired mobile robot navigation and planning. Frontiers in NeuroRobotics, 1 (2007)
Cuperlier, N., Quoy, M., Giovannangeli, C., Gaussier, P., Laroque, P.: Transition cells for navigation in an unknown environment, 286–297 (2006)
Etienne, A., Jeffery, K.: Path integration in mammals. Hippocampus, 180–192 (2004)
Franz, M., Schlkopf, B., Bltof, B.: Homing by parametrized scene matching. In: Advances in Artificial Life: Proc. of the European Conference on Artificial Life (1997)
Gaussier, P., Joulain, C.: A model of visual navigation: How to explain “place cells” and “view cells” activities. In: European Conference of Visual Perception ECVP 1998 (1998)
Gaussier, P., Joulain, C., Banquet, J., Leprtre, S., Revel, A.: The visual homing problem: An example of robotics/biology cross fertilization. Robotics and Autonomous System 30, 155–180 (2000)
Gaussier, P., Zrehen, S.: Perac: A neural architecture to control artificial animals. Robotics and Autonomous Systems 16(2), 291–320 (1995)
Giovannangeli, C., Gaussier, P.: Orientation system in robots: Merging allothetic and idiothetic estimations. In: 13th International Conference on Advanced Robotics (ICAR 2007), pp. 349–354 (2012)
Jauffret, A., Cuperlier, N., Tarroux, P., Gaussier, P.: From self-assessment to frustration, a small step toward autonomy in robotic navigation. Frontier in Neurobotics (2013)
Koch, C., Ullman, S.: Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Leprêtre, S., Gaussier, P., Cocquerez, J.: From navigation to active object recognition. In: The Sixth Int. Conf. on Simulation for Adaptive Behavior SAB 2000 (2000)
Martinelli, A., Tomatis, N., Siegwart, R.: Simultaneous localization and odometry self calibration for mobile robot. In: Autonomous Robots, vol. 22, pp. 75–85 (2006)
Panzieri, F.P.S., Ulivi, G.: Vision based navigation using kalman approach for slam. In: 11th. Int. Conf. on Advanced Robotics, Coimbra, Portugal (2003)
Redish, A., Elga, A., Touretzky, D.: Head direction cells in the deep cell layers of dorsal presubiculum in freely moving rats. Network, 10 (1984)
Redish, A., Elga, A., Touretzky, D.: A coupled attractor model of vision can sometimes predict a correct estimation, calibrating the rodent head direction system. Network, 7 (1996)
Rfer, T.: Controlling a wheelchair with image-based homing. In: AISB Symposium on Spatial Reasoning in Mobile Robots and Animals (1997)
Sheynikhovich, D., Grèzes, F., King, J.-R., Arleo, A.: Exploratory behaviour depends on multisensory integration during spatial learning. Artificial Neural Networks and Machine Learning 7552, 296–303 (2012)
Taube, J.S.: The head direction signal: Origins and sensory-motor integration. Annual Review of Neuroscience 30, 181–207 (2007)
Taube, J.S.: Head direction cells recorded in the anterior thalamic nuclei of freely moving rats. Journal of Neurosciences 15, 70–86 (1995)
Taube, J.S., Muller, R.U., Ranck Jr., J.B.: Head-direction cells recorded from the postsubiculum in freely moving rats. ii. effects of environmental manipulations. Journal of Neurosciences 10, 436–447 (1990)
Thrun, S.: Robotic mapping: A survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millenium, Morgan Kaufmann (2002)
Widrow, B., Hoff, M.E.: Adaptive switching circuits. IRE Wescon Convention Record 4, 96–104 (1960)
Zugaro, M.B., Arleo, A., Berthoz, A., Wiener, S.I.: Rapid spatial reorientation and head direction cells. Journal of Neurosciences 23, 3478–3482 (2003)
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Delarboulas, P., Gaussier, P., Caussy, R., Quoy, M. (2014). Robustness Study of a Multimodal Compass Inspired from HD-Cells and Dynamic Neural Fields. In: del Pobil, A.P., Chinellato, E., Martinez-Martin, E., Hallam, J., Cervera, E., Morales, A. (eds) From Animals to Animats 13. SAB 2014. Lecture Notes in Computer Science(), vol 8575. Springer, Cham. https://doi.org/10.1007/978-3-319-08864-8_13
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DOI: https://doi.org/10.1007/978-3-319-08864-8_13
Publisher Name: Springer, Cham
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