A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance

The fuzzy ant colony optimization (FACO) method proposed in this paper minimizes the iterative learning error of the ant colony optimization (ACO) algorithm using fuzzy control. This algorithm finds the shortest path, detects any obstructions in front of the mobile robot using ultrasonic transducers, and adjusts the turning angle of the mobile robot so as to avoid obstacles. To verify the FACO algorithm, simulations using a mobile robot in two environments were carried out. The first environment was shortest-path planning without obstacles. The second environment was shortest-path planning for a single destination with obstacles. This paper also compares tests carried out in a simple Z-shaped environment map and in a complicated environment map. By comparing FACO with a pattern search (PS) algorithm, a genetic algorithm (GA), particle swarm optimization (PSO), and traditional ACO for the cases in the simple Z-shaped environment map, we found that the path distance obtained using FACO was 2.60, 4.40, 2.04, and 6.53% shorter than that using PS, GA, PSO, and ACO, respectively. In the complex environment map, as compared to the self-adaptive ant colony optimization, FACO had a path distance that was 1.38% shorter. Therefore, the results showed that the FACO algorithm can find the shortest path and avoid obstacles for both simple and complex topographies.