By Topic

Simulated and situated models of chemical trail following in ants

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$15 $15
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

A simple neural network model for ant chemical trail following is proposed. It performs temporal differencing and comparison to control an osmotropotaxis response. The mechanism was first tested by implementing it in a virtual simulation, and using a genetic algorithm to find appropriate connection strengths. Resulting behavioural measures show strong similarity to data from ants and previous algorithmic models, including: non-linear effects of varying chemical strength; failure to follow trails crossed at large angles; worse trail following at faster speeds and better trail following with longer antennae. In a realworld implementation using chemical sensors on a robot following an alcohol-based trail, it was found necessary to use a somewhat different set of weightings to cope with the inherent unreliability of detecting chemical concentrations. It was still possible to show qualitatively similar behaviour under the same experimental conditions as the simulation model. We argue that these results may illustrate the nature of the agent-environment task space rather than proving the model ‘correct’; but that the model nevertheless provides useful pointers to further investigation of this biological system.