International Journal of Intelligent Mechatronics and Robotics, 2(1), 57-71, January-March 2012 57 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Ants, Autonomous Robots, Bio-Inspired Robotics, Navigation, Neural Networks 1. INTRODUCTION The ability to navigate in a complex environment is crucial for both animals and robots. Social insects routinely return to the colony after their foraging excursions, on long and winding routes (Wilson, 1971). Given their size and relatively simple nervous systems, it seems likely that insects use simpler and economical ways to navigate in the real world. An ant, for example, navigates tirelessly back to its nest after going out on foraging expeditions several thousand body lengths away from its nest, relying mainly on olfactory and visual cues when navigating over large distances. Similar principles can be applied to the design of navigational strategies for autonomous robots that may be deployed to perform tasks such as: foraging (Lerman & Galstyan, 2002); gathering (Jiménez, Shirinza- deh, Oetomo, & Nicholson, 2011); and flocking (Egerstedt & Hu, 2001). When returning to the nest ants navigate by Path Integration (PI), chemical navigation (trail following), and visual navigation (landmark fol- On the Development of an Ants-Inspired Navigational Network for Autonomous Robots Paulo A. Jiménez, Monash University, Australia Yongmin Zhong, RMIT University, Australia ABSTRACT Experimental research in biology has uncovered a number of different ways in which ants use environmen- tal cues for navigational purposes. For instance, pheromone trail laying and trail following behaviours of ants have proved to be an effcient mechanism to optimise path selection in natural as well as in artifcial 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. DOI: 10.4018/ijimr.2012010104