AI-Assisted Swarm Robotics for Autonomous Exploration Using Ant Colony Algorithms

This study explores the integration of Artificial Intelligence (AI) techniques with Ant Colony Optimization (ACO) to enhance swarm robotic exploration in dynamic and uncertain environments. While traditional ACO provides a decentralized, pheromone-based mechanism for path planning, it suffers from slow convergence, stagnation, and limited adaptability when confronted with environmental changes. To address these challenges, an AI-assisted ACO framework was developed, incorporating RL principles to adapt pheromone update rules in real time. Robots learn from past interactions, adjust strategies dynamically, and maintain efficient exploration under varying conditions. Python-based simulations were conducted across four environments—50×50, 100×100, 250×250, and 500×500 grids—with swarm sizes ranging from 20 to 200 robots and obstacle densities of 10%, 20%, and 30%. Results show that AI-assisted ACO consistently outperformed traditional ACO, achieving 7–15% higher exploration coverage (≈ 52.1% vs. 44.6% in the baseline case and ≈ 61% vs. 48% in the largest grid), with ≈ 20–25% faster convergence and ≈ 25% higher adaptability under dynamic obstacle scenarios.