Divergent stereo in autonomous navigation: from bees to robots
International Journal of Computer Vision - Special issue on qualitative vision
Simulated and situated models of chemical trail following in ants
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Autonomous Robots
Insect Inspired Behaviours for the Autonomous Control of Mobile Robots
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
The mixture of neural networks adapted to multilayer feedforward architecture
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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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.