Abstract
Robotic collision detection is a complex task that requires both real time data acquisition and important features extraction from a captured image. In order to accomplish this task, the algorithms used need to be fast to process the captured data and perform real time decisions. Real-time collision detection in dynamic scenarios is a hard task if the algorithms used are based on conventional techniques of computer vision, since these arecomputationally complex and, consequently, time-consuming, specially if we consider small robotic devices with limited computational resources. On the other hand, neurorobotic models may provide a foundation for the development of more effective and autonomous robots, based on an improved understanding at the biological basis of adaptive behavior. Particularly, our approach must be inspired in simple neural systems, which only requires a small amount of neural hardware to perfom complex behaviours and, consequently, becomes easier to understand all the mechanism behind these behaviours. By this reason, flying insects are particularly attractive as sources of inspiration due to the complexity and efficiency of the behaviours allied with the simplicity of a reduced neural system. The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Locust optic lobe. It responds selectively to looming objects and can trigger avoidance reactions when a rapidly approaching object is detected. Based on the relatively simple encoding strategy of the LGMD neuron, different bio-inspired neural networks for collision avoidance were developed. In the work presented in this chapter, we propose a new LGMD model based on two previous models, in order to improve over them by incorporating other features. To accomplish this goal, we proceed as follows: (1) we critically analyse different LGMD models proposed in literature; (2) we highlight the convergence or divergence in the results obtained with each of the models; (3) we merge the advantages/disadvantages of each model into a new one. In order to assess the real-time properties of the proposed model, it was applied to a real robot. The obtained results have shown the high capability and robustness of the LGMD model to prevent collisions in complex visual scenarios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gray, J.R., Lee, J.K., Robertson, R.M.: Activity of descending contralateral movement detector neurons and collision avoidance behaviour in response to head-on visual stimuli in locusts. J. Comp. Physiol. A 187(2), 115–129 (2001)
Rind, F.C.: Non-directional, movement sensitive neurones of the locust optic lobe. J. Comp. Physiol. A 161(3), 477–494 (1987)
Gabbiani, F., Krapp, H., Laurent, G.: Computation of object approach by a wide-field motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999)
Gray, J.R., Blincow, E., Robertson, R.: A pair motion-sensitive neurons in the locust encode approaches of a looming object. J. Comp. Physiol. A 196(12), 927–938 (2010)
Rind, F.C., Bramwell, D.I.: Neural network based on the input organization of an identified neuron signaling impeding collision. J. Neurophysiol. 75(3), 967–985 (1996)
Blanchard, M., Rind, F.C., Verschure, P.F.M.J.: Collision avoidance using a model of the locust LGMD neuron. Robot. Auton. Syst. 30(1), 17–37 (2000)
Yue, S., Rind, F.C.: Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement. IEEE Trans. Neural Netw. 17(3), 705–716 (2006)
Stafford, R., Santer, R.D., Rind, F.C.: A bio-inspired visual collision detection mechanism for cars: combining insect inspired neurons to create a robust system. BioSystems 87, 164–171 (2007)
Meng, H., Yue, S., Hunter, A., Appiah, K., Hobden, M., Priestley, N., Hobden, P., Pettit, C.: A modified neural network model for the lobula giant movement detector with additional depth movement feature. In: Proceedings of International Joint Conference on Neural Networks, pp. 14–19. Atlanta, Georgia (2009)
Guest, B.B., Gray, J.R.: Respones of a looming-sensitive neuron to compound and paired object approaches. J. Neurophysiol 95(3), 1428–1441 (2006)
Gabbiani, F., Mo, C., Laurent, G.: Invariance of angular threshold computation in a wide-field looming-sensitive neuron. J. Neurosci. 21(1), 314–329 (2001)
Gabbiani, F., Krapp, H.G., Koch, C., Laurent, G.: Multiplicative computation in a visual neuron sensitive to looming. Nature 420, 320–324 (2002)
Badia, S.B.i, Bernardet, U, Verschure, P.F.M.J.: Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector. PLOS Comput. Biol. 6(3), e1000701 (2010)
Acknowledgments
Ana Silva is supported by Ph.D. Grant SFRH/BD/70396/2010. This work is funded by FEDER Funds through the Operational Programme Competitiveness Factors—COMPETE and National Funds through FCT—Foundation for Science and Technology under the Project: FCOMP-01-FEDER-0124-022674.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Silva, A.C., Silva, J., Santos, C.P.d. (2014). A Modified LGMD Based Neural Network for Automatic Collision Detection. In: Ferrier, JL., Bernard, A., Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-319-03500-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-03500-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03499-7
Online ISBN: 978-3-319-03500-0
eBook Packages: EngineeringEngineering (R0)