Research Of Path Planning For Mobile Robots Based On Immune Genetic Algorithm

Genetic Algorithm (GA) is a kind of novelty evolution algorithm and GA also is a population based optimization tool. The system is initialized with a population of random solutions and searches for optima by updating generations. GA has been successfully applied in many areas: function optimization, artificial neural network training, fuzzy system control, and other areas. Its superiority in solving complex problem has been manifested.The origin and the development of Genetic Algorithm are outlined in this paper: The principal, the biological mechanisms, the characteristics and the application of the GA are presented, Meanwhile, some classical improved GA to make up for the shortcomings of GA and its widespread applications have been introduced. On the basis of that, a new method--Immune Genetic Algorithm based on GA was proposed. The origin and the development of mobile robot are also outlined in this paper. The mobile robot's prospect aspect in the future--intelligent robot has been presented. The path planning for mobile robots is the most important aspect of intelligent robot. The general conceptions, characteristic, classify based issue and some familiar methods of path planning are presented.On the basis of GA, according to the characteristics of the path planning of mobile robots in the static environment, a new method-Immune Genetic Algorithm with Elitism (IGAE)--was proposed. This method is used to settle some major drawbacks of GA such as premature, sticks to local optimum easily, low local search ability and slow speed of convergence. The path planning of mobile robots based on IGAE includes two steps: the first step is adopting the grid theory to establish the free space model of the mobile robot; the second step is adopting the Immune Genetic Algorithm to find out the global optimal path. The computer simulation experiment was carried out. By comparing with the path planning method based on the GA, it has been confirmed that the IGAE has better performance in convergence speed, dynamic convergence behavior.