An improved ant colony system algorithm for robot path planning and performance analysis

Abstract

An improved ant colony system (ACS) algorithm to solve the mobile robot path planning problem is presented. In the algorithm, a new heuristic operator is adopted to achieve a balance between population diversity and the convergence rate. It complements the algorithm to avoid running into the local optimum and to improve the solution quality. A heuristic path selection strategy is proposed to guide the algorithm to fast convergence. We adopt the MAKLINK graph and grids to establish the environment model, and the simulation research indicates that the proposed algorithm is effect. It can improve the solution quality and has better performance in search efficiency compared with other path planning methods. We also analyse the performance of the modified ACS algorithm and demonstrate that the novel algorithm can obtain the optimal solution for mobile robot path planning problems with faster convergence speed and better solution quality under different complex environments.