A New Robot Navigation Algorithm Based on Bi-Directional Collaborative Ant Colony Optimization

Abstract

Swarm intelligence has been widely adopted and successfully applied in the field of autonomous robot navigation. Among various swarm intelligence algorithms, ant colony optimization (ACO) has shown significant potential in addressing complex navigation challenges. However, ACO faces challenges, for example, unclear initial search direction, slow convergence, limited ant flexibility, and the need to simplify robot motion control. To address these challenges, this article presents a novel bi-directional collaborative ACO (BC-ACO) algorithm with key innovations. First, the algorithm adopts a bi-directional ant colony with forward and reverse populations, achieving effective route planning through collaborative decision-making. Second, the algorithm employs an adaptive step-size strategy and a stage-based exploration ant quantity adjustment method. These innovations optimize the balance between exploration and exploitation, accelerate convergence, and address the inefficiencies of traditional ACO methods. Additionally, this article improves the heuristic function by integrating a node distance index within the bi-directional ant colony, guiding the transition of ants between nodes and further accelerating convergence. Simulation results show that BC-ACO reduces the computation time by 73.97% and improves the convergence stability by 63.64% compared to standard ACO. Additionally, BC-ACO successfully plans optimal paths, achieving a 20.85% reduction in path length. Further integration of a local quadratic segmented B-spline (LQ-S B-spline) curve results in paths with smooth transitions, reducing robot motion complexity. In summary, BC-ACO achieves fast global convergence, high stability, and short computation time.