Applying an Ant Colony Optimization Algorithm to an Artificial Vision Problem in a Robotic Vehicle

In this paper, a problem of artificial vision in a robotic autonomous vehicle consisting of the real time detection and tracking of non-structured roads is addressed by applying an Ant Colony Optimization (ACO) algorithm. The solution adopted tries to find some properties describing the probability that a pixel belongs to the boundaries of the road, and then formalize the road detection problem as an optimization one.

[1]  Charles E. Thorpe,et al.  UNSCARF-a color vision system for the detection of unstructured roads , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[2]  Sebastian Thrun,et al.  Self-supervised Monocular Road Detection in Desert Terrain , 2006, Robotics: Science and Systems.

[3]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[4]  Sumit Ghosh,et al.  A Study of Synthetic Creativity: Behavior Modeling and Simulation of an Ant Colony , 2000, IEEE Intell. Syst..

[5]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

[6]  J. Deneubourg,et al.  Probabilistic behaviour in ants: A strategy of errors? , 1983 .

[7]  Alberto Broggi,et al.  An agent based evolutionary approach to path detection for off-road vehicle guidance , 2006, Pattern Recognit. Lett..

[8]  Luis Magdalena,et al.  A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads , 2004, Auton. Robots.

[9]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[10]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.