Bio-Inspired Real-Time Robot Vision for Collision Avoidance

Hirotsugu Okuno; Tetsuya Yagi

doi:10.20965/jrm.2008.p0068

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JRM Vol.20 No.1 pp. 68-74

doi: 10.20965/jrm.2008.p0068

(2008)

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Bio-Inspired Real-Time Robot Vision for Collision Avoidance

Hirotsugu Okuno and Tetsuya Yagi

Division of Electrical, Electronic and Information Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

Received:

March 22, 2007

Accepted:

July 2, 2007

Published:

February 20, 2008

Keywords:

collision avoidance, robot vision, bio-inspired, silicon retina

Abstract

A mixed analog-digital integrated vision sensor was designed to detect an approaching object in real-time. To respond selectively to approaching stimuli, the sensor employed an algorithm inspired by the visual nervous system of a locust, which can avoid collisions robustly by using visual information. An electronic circuit model was designed to mimic the architecture of the locust nervous system. Computer simulations showed that the model provided appropriate responses for collision avoidance. We implemented the model with a compact hardware system consisting of a silicon retina and field-programmable gate array (FPGA) circuits; the system was confirmed to respond selectively to approaching stimuli that constituted a collision threat.

Cite this article as:

H. Okuno and T. Yagi, “Bio-Inspired Real-Time Robot Vision for Collision Avoidance,” J. Robot. Mechatron., Vol.20 No.1, pp. 68-74, 2008.

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References

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[18] R. Laviana, L. Carranza, S. Vargas, G. Liñán, and E. Roca, “A Bioinspired Vision Chip Architecture for Collision Detection in Automotive Applications,” Proc. of the SPIE Conf. on Bioengineered and Bioinspired System, 2005.
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[20] R. Takami, K. Shimonomura, S. Kameda, and T. Yagi, “A novel preprocessing vision system employing neuromorphic 100x100 pixel silicon retina,” Proc. 2005 IEEE Intl. Symp. on Circuits and Systems, pp. 2771-2774, Kobe, Japan, 2005.
[21] S. Kameda and T. Yagi, “An analog VLSI chip emulating sustained and transient response channels of the vertebrate retina,” IEEE Trans. on Neural Networks, Vol.14, pp. 1405-1412, 2003.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Y. Wang and B. J. Frost, “Time to collision is signalled by neurons in the nucleus rotundus of pigeons,” Nature, 356, pp. 236-238, 1992.

[2] [2] H. Sun and B. J. Frost, “Computation of different optical variables of looming objects in pigeon nucleus,” Nat. Neurosci., Vol.1, pp. 296-303, 1998.

[3] [3] F. C. Rind and P. J. Simmons, “Seeing what is coming: building collision-sensitive neurons,” Trends Neuroscience, 22(5), pp. 215-220, May 1999.

[4] [4] W. Reichardt and T. Poggio, “Visual control of orientation behaviour in the fly Part I,” Q. Rev. Biophys., Vol.9, pp. 311-375, 1976.

[5] [5] T. Poggio and W. Reichardt, “Visual control of orientation behaviour in the fly Part II,” Q. Rev. Biophys., Vol.9, pp. 377-438, 1976.

[6] [6] M. Egelhaaf and A. Borst, “A Look into the Cockpit of the Fly: Visual Orientation, Algorithms, and Identified Neuron,” J. Neurosci., Vol.13, pp. 4563-4574, 1993.

[7] [7] N. Franceschini, J. M. Pichon, and C. Blanes, “From insect vision to robot vision,” Philos. Trans. Roy. Soc. Lond. B, Vol.337, pp. 283-294, 1992.

[8] [8] N. Franceschini, “Visual guidance based on optic flow: a biorobotic approach,” J. Physiol. Paris, Vol.98, pp. 281-292, 2004.

[9] [9] B. Webb, “Robots in invertebrate neuroscience,” Nature, Vol.417, pp. 359-363, 2002.

[10] [10] N. Hatsopoulus, F. Gabbiani, and G. Laurent, “Elementary computation of object approach by a wide-field visual neuron,” Science, Vol.270, pp. 1000-1003, 1995.

[11] [11] F. C. Rind, “Intracellular characterization of neurons in the locust brain signalling impending collision,” J. Neurophysiol., Vol.75, pp. 986-995, 1996.

[12] [12] M. O’Shea and J. L. Williams, “The anatomy and output connections of a locust visual interneuron: the lobula giant movement detector (LGMD) neuron,” J. Comp. Physiol., Vol.91, pp. 257-266, 1974.

[13] [13] F. C. Rind and D. I. Bramwell, “Neural network based on the input organization of an identified neuron signaling impending collision,” J. Neurophysiol., Vol.75, pp. 967-984, 1996.

[14] [14] M. Blanchard, F. C. Rind, and P. F. M. J. Verschure, “Collision avoidance using a model of the locust LGMD neuron,” Robot. Auton. Sys., Vol.30, pp. 17-38, 2000.

[15] [15] S. Bermudez and P. Verschure, “A Collision Avoidance Model Based on the Lobula Giant Movement Detector (LGMD) neuron of the Locust,” Proc. of the IJCNN, Budapest, 2004.

[16] [16] S. Yue, F. C. Rind, M. S. Keil, J. Cuadri, and R. Stafford, “A bioinspired visual collision detection mechanism for cars: Optimisation of a model of a locust neuron to a novel environment,” Neuro-Computing, Vol.69, pp. 1591-1598, 2006.

[17] [17] J. Cuadri, G. Linan, R. Stafford, M. S. Keil, and E. Roca, “A bioinspired collision detection algorithm for VLSI implementation,” Proc. of the SPIE Conf. on Bioengineered and Bioinspired System, 2005.

[18] [18] R. Laviana, L. Carranza, S. Vargas, G. Liñán, and E. Roca, “A Bioinspired Vision Chip Architecture for Collision Detection in Automotive Applications,” Proc. of the SPIE Conf. on Bioengineered and Bioinspired System, 2005.

[19] [19] G. Indiveri and R. Douglas, “Neuromorphic Vision Sensors,” Science, Vol.288, pp. 1189-1190, 2000.

[20] [20] R. Takami, K. Shimonomura, S. Kameda, and T. Yagi, “A novel preprocessing vision system employing neuromorphic 100x100 pixel silicon retina,” Proc. 2005 IEEE Intl. Symp. on Circuits and Systems, pp. 2771-2774, Kobe, Japan, 2005.

[21] [21] S. Kameda and T. Yagi, “An analog VLSI chip emulating sustained and transient response channels of the vertebrate retina,” IEEE Trans. on Neural Networks, Vol.14, pp. 1405-1412, 2003.