Research For The Path Planning Of Mobile Robot Based On Improved Ant Colony Algorithm

The research achievements of multiple disciplines and technologies on Intelligent mobile robot, has become in recent years, mainly in aspects of intelligence, integration of electrical,represents an important aspect of the research results.. The path planning is one of the important content in the field of robotics research, is the necessary foundation to realize intelligent robot operation. Generally divided into environment fully known static path planning and the environment map part known or all unknown dynamic path planning. This article main research of the former analysis.Ant colony algorithm is a kind of new heuristic bionic algorithm for solving combinatorial optimization problems, was first used to solve the traveling salesman problem. Since the effective sorting on famous traveling salesman problem(TSP) and artifacts problem, ant colony algorithm has been penetrated into various fields, now the application on mobile robot path planning is also very extensive.For the ant colony algorithm path planning problem, many scholars has done a lot of work,but the basic ant colony algorithm execution rely on large number of iterations and circulation,lack of real-time, pheromone accumulation during the process of operation, quality problems such as path does not highlight, still needs further research.In this paper, the concrete research content is as follows:Analysis and comparison the commonly used environment map building method, choose suitable map form for ant colony algorithm path planning(grid method); Simulation analysis the basic ant colony algorithm path planning, find out the problem of the basic ant colony algorithm applied on path planning(the algorithm running time is long, instability and so on); Improve the basic ant colony algorithm by using the artificial potential field method, max-min ant colony system, crossover and mutation operators, elite sorting method and the pheromone track smooth;Simulation analysis the influence on the efficiency of path planning on ant colony algorithm by the main parameter like the ant colony number m, the information incentive factors ?, the expected incentive factors ? and the pheromone volatilization coefficient, select the optimal portfolio parameter contrapose this paper; Simulation analysis the experimental results of the improved algorithm(2D and 2D).This paper has completed all of the above experiment content, through the simulation analysis,we can know that the improved optimal path length is shortened by 12.56%; the convergence algebra fell by 55.86%; the algorithm time is decreased by 65.3%; the optimal solution percentage increased by 40%. The algorithm performance improve on convergence speed, the algorithm time and the proportion of the optimal solution is the most significant significantly. Path planning in three-dimensional space also got very good effect. Proved that the improvement of the basic ant colony algorithm in this paper is effective and feasible.