Design and Simulation of a Collision-free Path Planning Algorithm for Mobile Robots Based on Improved Ant Colony Optimization

This paper attempts to solve the 2D global path planning problem in a known environment. For this purpose, a smooth path planning method was designed for mobile robots based on dynamic feedback A* search algorithm and the improved ant colony optimization (ACO). Specifically, the ACO was improved from three aspects: optimizing the initial pheromone, improving evolutionary strategy and implementing dynamic closed-loop adjustment of parameters. The planned path was then smoothened by the cubic B-spline curve. The simulation results show our method converged to a shorter path in less time than the original ACO, and avoided the local optimum trap.