Path planning of mobile Robot based on improved ant colony algorithm based on Honeycomb grid

Abstract

Based on the problems of traditional ant colony algorithm in complex environment such as slow convergence speed and easily falling into local optimal value, this paper proposes an improved ant colony algorithm based on honeycomb grid. Firstly, a grid map based on honeycomb grid was established to solve the problem of non-uniformity of traditional grid step size. Secondly, according to the traditional ant colony algorithm blindly search for the path in the early stage, centering on the straight line from the starting point to the end point, the design of uneven distribution of initial pheromone concentration was carried out by integrating three factors, namely the length from the ideal line, the number of obstacle grids and the target deviation Angle, to avoid the blind search in the early planning, improve the search efficiency in the early stage, and alleviate the problem of ant death. Thirdly, an adaptive attenuation coefficient is introduced to improve the heuristic function to strengthen the guiding role of pheromones in the later period and accelerate the convergence speed. The safety factor is introduced into the transfer probability to reduce the ant "deadlock" problem. Then, the updating rules of pheromones are improved by combining ant colony sequencing model, and the critical range of pheromones is set to improve the global optimization ability. The simulation results show that the proposed algorithm can improve the global search capability, and the path length and turning point can be reduced by 17.12% and 55.56% respectively compared with the traditional algorithm. The effectiveness of the proposed algorithm is verified.