Mobile Robot Path Planning Using Ant Colony Optimization Algorithm

In this paper, we implement the Ant Colony Optimization (ACO) algorithm for mobile robot path planning. This bio-inspired algorithm efficiently obtains global optimal solutions by simulating ant foraging behavior. The core mechanism involves artificial ants depositing pheromones during path exploration, where higher pheromone concentrations on optimal paths guide subsequent ants through positive feedback. Key implementation components include: 1) Environment grid discretization using matrix representation, 2) Probabilistic path selection based on pheromone intensity and heuristic visibility, 3) Pheromone update rules combining evaporation and reinforcement mechanisms. The algorithm demonstrates particular effectiveness in complex environments due to its parallel computation characteristics and rapid convergence properties. Our MATLAB implementation features modular functions for colony initialization, path cost calculation, and dynamic pheromone management, achieving satisfactory results in balancing solution quality and computational efficiency for mobile robot navigation challenges.