Toward the Formal Foundation of Ant Programming
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
Computers and Industrial Engineering
A swarm cognition realization of attention, action selection, and spatial memory
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Tracking natural trails with swarm-based visual saliency
Journal of Field Robotics
Semantic-based approach for route determination and ontologyupdating
Engineering Applications of Artificial Intelligence
Neural-swarm visual saliency for path following
Applied Soft Computing
Recent progress in road and lane detection: a survey
Machine Vision and Applications
| Hi-index | 0.00 |
This paper describes an ant colony optimization approach adopted to decide on road-borders to automatically guide a vehicle developed for the DARPA Grand Challenge 2004, available from: . Due to the complexity of off-road trails and different natural boundaries of the trails, lane markers detection schemes cannot work. Hence border detection is based on ant colony optimization technique. Two borders at two sides of the road (as seen by a camera fixed on the vehicle) are tracked by two agent colonies: agents' moves are inspired by the behaviors of biological ants when trying to find the shortest path from nest to food. Reinforcement learning is done by pheromone updating based on some heuristic function and by changing the heuristic balancing parameters with the experience over the last tracked results. Shadow removal has also been introduced to increase robustness. Results on different off-road environments, as provided in the DARPA Grand Challenge 2004, have been shown in the form of correct detections, false positives and false negatives and their dependence on number of ant-agents and balancing edge-exploitation and pheromone-exploitation.