Real-Time Path Planning for Autonomous Robots Using Ant Colony Optimization

Abstract

This paper explores the application of Ant Colony Optimization (ACO) for real-time path planning in autonomous robots. ACO, inspired by the foraging behavior of ants, is utilized to navigate robots through dynamic environments. This study evaluates the efficiency, adaptability, and practicality of ACO in comparison to traditional path planning algorithms, particularly in scenarios requiring quick decision-making and real-time response. Experimental results demonstrate that ACO can effectively manage complex environments and adjust to dynamic changes, making it a viable solution for real-time robotic navigation.