Robot Path Planning Using Ant Colony Optimization Algorithm

This paper investigates global path planning methodologies for robots operating in static environments. The approach utilizes grid-based decomposition for environment abstraction and establishes a structured robot workspace model. The core algorithm implements Ant Colony Optimization (ACO) to emulate ant pheromone trail deposition and evaporation mechanisms, enabling efficient path搜索 from designated start to end points. MATLAB implementation features: - Grid environment initialization using matrix representations - Pheromone update functions with evaporation coefficient control - Probabilistic path selection based on heuristic information and pheromone concentrations - Convergence criteria implementation for optimal path identification Simulation results from three distinct static environment configurations validate key parameter selections, including pheromone influence factors and exploration-exploitation balance. Comparative analysis with Genetic Algorithm (GA) implementations demonstrates ACO's advantages in computational efficiency and memory utilization. The research confirms ACO's practical viability for autonomous robot navigation systems, highlighting its significance for intelligent control applications in static environments.