Hybrid Ant Colony and Q-Learning Algorithm for Swarm Robots: Path Planning and Collision Avoidance in Unknown Environments

This paper presents a hybrid approach combining Ant Colony Optimization (ACO) and Q-learning for swarm robot path planning and collision avoidance in unknown environments. Traditional ACO-based path planning methods face challenges such as local minima and suboptimal solutions when navigating into complex environments. By integrating Q-learning, the proposed method enhances adaptive decision-making, enabling robots to dynamically explore and optimize their routes. The epsilon-greedy strategy balances exploration and exploitation, preventing robots from getting stuck in dead zones and allowing for the discovery of more efficient paths. Simulations conducted in MATLAB demonstrate that the ACO-Q-learning algorithm generates multiple alternative paths, improves overall path efficiency, and enhances swarm coordination compared to standard ACO and Artificial Potential Field (APF) methods. The results highlight the algorithm's potential for real-world swarm robotics applications, where decentralized robots must autonomously navigate and adapt to changing environments.