The Multi-Strategy based Ant Colony Algorithm for the Path Planning of Mobile Robots

Abstract

To address the issues of slow convergence speed in the early stage, rapid decrease of diversity, and a tendency to get stuck in local optima in traditional Ant Colony Optimization algorithms for mobile robot path planning, a composite Multi-strategy improved Ant Colony Optimization algorithm is proposed. In a two-dimensional plane, the mobile robot working environment is created using a grid method. The L-M trust region strategy is used to plan the initial information weight of the path map to reduce the blindness of ant colony path search in the early stage. Next, a new heuristic function is constructed based on multi-factor induction strategy to reduce the probability of the ant colony getting stuck in local optima. Finally, the “lion king rule” strategy is used to improve the updating method of information weight. Experimental results show that the improved Ant Colony Optimization algorithm effectively improves the early stage convergence speed, has good global optimization performance, and verifies the feasibility and superiority of the improved Ant Colony Optimization algorithm in two-dimensional space path planning.