MATLAB Implementation of Guidance-Factor-Free UAV Path Planning Using Ant Colony Optimization Algorithm

The MATLAB program for UAV path planning using a guidance-factor-free ant colony optimization algorithm presented in this research provides an efficient computational approach. This implementation utilizes ant colony optimization (ACO) metaheuristics to autonomously generate optimal UAV flight paths based on specified objectives and constraints. The core algorithm mimics natural ant behavior through probabilistic path selection, pheromone updating mechanisms, and evaporation processes to iteratively improve solution quality. Key implementation features include: distance calculation functions for path cost evaluation, pheromone matrix initialization and updating procedures, probabilistic transition rules for path selection, and constraint handling mechanisms for obstacle avoidance. Researchers can leverage this program to thoroughly understand UAV path planning principles and methodologies, while practical applications benefit from its capability to generate feasible and optimized flight trajectories. The code structure allows customization of parameters including number of ants, iteration counts, evaporation rates, and heuristic information weights for specific mission requirements.