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On the Development of an Ants-Inspired Navigational Network for Autonomous Robots

On the Development of an Ants-Inspired Navigational Network for Autonomous Robots

Paulo A. Jiménez, Yongmin Zhong
Copyright: © 2012 |Volume: 2 |Issue: 1 |Pages: 15
ISSN: 2156-1664|EISSN: 2156-1656|EISBN13: 9781466613034|DOI: 10.4018/ijimr.2012010104
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MLA

Jiménez, Paulo A., and Yongmin Zhong. "On the Development of an Ants-Inspired Navigational Network for Autonomous Robots." IJIMR vol.2, no.1 2012: pp.57-71. http://doi.org/10.4018/ijimr.2012010104

APA

Jiménez, P. A. & Zhong, Y. (2012). On the Development of an Ants-Inspired Navigational Network for Autonomous Robots. International Journal of Intelligent Mechatronics and Robotics (IJIMR), 2(1), 57-71. http://doi.org/10.4018/ijimr.2012010104

Chicago

Jiménez, Paulo A., and Yongmin Zhong. "On the Development of an Ants-Inspired Navigational Network for Autonomous Robots," International Journal of Intelligent Mechatronics and Robotics (IJIMR) 2, no.1: 57-71. http://doi.org/10.4018/ijimr.2012010104

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Abstract

Experimental research in biology has uncovered a number of different ways in which ants use environmental cues for navigational purposes. For instance, pheromone trail laying and trail following behaviours of ants have proved to be an efficient mechanism to optimise path selection in natural as well as in artificial situations. Drawing inspiration from biology, the authors present a new neural strategy for navigation. The authors propose a navigational network composed of a gating network, memory and two recurrent neural networks (RNN). The navigational network learns to follow a trail and to orientate based on landmarks, while continuously recording the location of the home position in case the trail is lost. The orientation was encoded as a continuous ring of neurons, while the distance was encoded as a chain of neurons. Finally, the computational analysis provides a more complete exploration of the properties of the proposed navigational network. This network is able to learn and select behaviours based on sensory clues. The proposed model shows that neural path integration is possible and is easy to achieve.

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