Research On Path Planning Of Mobile Robot Based On Adaptive Ant Colony Algorithm

Since the rise of the robot in 1960s, people have been interested in it, and the research of the robot has been deeply studied. Robot technology is of great significance to promote the development of human society and the liberation of human labor. Along with the continuous development of modern science and technology and intelligent level, the application of robot can be penetrated into all aspects of social life, especially in aerospace, deep-sea exploration and service robot development is reached unprecedented heights. So the robot’s performance such as security、 stability、intelligence level、work efficiency and so on are more concerned by people.First of all, this paper describes the significance of the path planning of mobile robots, the background of path planning and application area, the research status at home and abroad, and the development level of the robot. The origin and the principle of ant colony algorithm for mobile robot path planning are analyzed, the mathematical model is established, and the evaluation index and the advantages and disadvantages of the path planning of mobile robot based on ant colony algorithm are described. The environmental space model of path planning is established, define and describe the specific amount of the algorithm involved in, the defect on the algorithm itself is improved, the deficiencies of the search strategy is made up by fallback strategy, the simulation experiment is carried out on the traditional ant colony algorithm and the improved ant colony algorithm, and the experimental results are analyzed.The ant colony algorithm is proposed through the observation and research of the behavior of ants foraging, has a good application in the path planning of the mobile robot. Based on the model of the TSP problem of after N cities, the behavior of the actual ant colony is simulated, and the path selection probability model of the ant colony algorithm is obtained. Influencing the path probability selection heuristic factors a and βand pheromone concentration coefficient ρare experimental studied, and the optimal value is selected. The TSP problem is simulated by the adaptive ant colony algorithm, and the results are compared and analyzed.Finally, adaptive ant colony algorithm is used to plan the path of mobile robot. By combining genetic algorithm to avoid the traditional ant colony algorithm into the local optimal value, combined with the parameters of the ion swarm optimization algorithm to optimize and filter, the adaptation degree of the algorithm is improved. Simulation experiments are carried out on the shortest path, the convergence speed and the global path graph, the effectiveness of adaptive ant colony algorithm is further verified by changing the magnification of pheromone concentration and the location of obstacles on the path, test results showed that the adaptive ant colony algorithm is superior to the traditional ant colony algorithm.