Research On Path Planning Of Mobole Robot Based On Improved Ant Colony Algorithm

In recent years,with the development of science and technology and the advancement of social sciences,the new generation of artificial intelligence products mobile robots are widely used in production,manufacturing,food industry and service industries and other aspects of production and life.The rapid development of mobile robots has received great attention from international scientific research.Path planning is an important prerequisite for mobile robots to move from start point to end point quickly,accurately and smoothly.How to make path planning perfectly and design related algorithms to make mobile robots move fast,efficiently,completely of tasks is an urgent problem to be solved.Nowadays,although there are many algorithms or improved algorithms for solving mobile robot path planning problems,there are still no most effective methods to overcome multiple problems at the same time.Therefore,it is important and practical value for all aspects of production and life to make research on the path planning algorithm for mobile robots.Based on the current research situation of mobile robots at home and abroad,this paper fully investigates the application research of various industrial manufacturing robots and their development trends,and carefully explores the advantages and disadvantages of conventional algorithms for path planning,and the structure and work of mobile robot systems.The principle has been fully recognized.The advantages of ant colony algorithm and artificial potential field are combined.The environment model and mathematical model are established,and the simulation is carried out by using python software.The main work of this paper are as follows:Firstly,the advantages and disadvantages of the basic methods used to solve the path planning problem of mobile robots are studied in depth.Then the source and basic ideas of traditional ant colony algorithm are explored,and the mathematical models and advantages and defects of mobile robot path planning are elaborated.Then,the execution flow of the algorithm is designed.Secondly,the advantages and disadvantages of artificial potential field and the advantages of combining with ant colony algorithm are analyzed in depth.The influence of ant colony algorithm parameters on performance is obtained through simulation analysis,and the selection of parameters is determined.The combination of algorithms the artificial potential field function and improved ant colony enhances the local exploration ability of the algorithm;the introduction of the excitation function makes the ants more inclined to select fewer obstacles in the search path,which improved the smoothness of the path and reduced the number of turns of the mobile robot;The improved of pheromone allocation mechanism improved the influence of excellent ants on the algorithm and reduced the damage of the bad ant to the algorithm,and improved the global search ability and convergence speed of the algorithm.Finally,the force of the mobile robot on the virtual artificial potential field is analyzed,and the diffusion model and algorithm of the local pheromone are extracted.The ant colony algorithm is further improved and simulated in Python to verify the rationality of the improved algorithm.The simulation results show that the introduction of local pheromone diffusion algorithm can increase the diversity of the solution.The global search ability of the algorithm can be improved in the initial exploration,which also accelerates the convergence speed of the algorithm.The heuristic function is improved to make the current point and the next point.The end-point is correlated to reduce the length of the ant colony search path;the dynamic volatility strategy of the pheromone is improved,and the possibility that the ant is trapped in the local optimal solution is reduced.