Path Planning Based On Immune Evolution And Chaotic Mutation For Mobile Robot

With the continuous progress of robot's technology, mobile robot represent the development of information technology,automation technology,system integration technology to some extent.The thesis makes a summary on different research aspects of mobile robot and contrast the different development level of foreign and domestic. In particular, path planning is important to mobile robot system because it determines the quality of the robot's task. As a result, path planning has attained more and more attention in the field of mobile robot. Several methods such as potential field method,grid method,genetic algorithm method, which used in mobile robot's obstacle avoidance and path planning are described in detail.According to the characteristic of path planning problem, every component of the algorithms are analyzed carefully, including chromosome representation,path evaluation,genetic operators design and GA parameters selection. A new genetic algorithm is presented by use of genetic algorithm to optimize the valid path generated by numerical potential method. Using the concept of immune evolution applying to path planning.In the following context, we use the genetic simulated annealing algorithm to carry out global path planning in static. Before path planning process, robot's working environment is built by vertex method. In the algorithm, a simple real number coding technique is used to accelerate the search of optimum path. The complicated two-dimensional path-coding problem is reduced to a simple one-dimensional problem in the coding scheme. Large-scale initialization combined with the selecting mechanism is applied to initialize the starting point out of the barrier area. The feasibility,smoothness and length of a path determine an efficient adaptive function. Select strategy uses proportion select method; crossover operator is one-point crossover strategy. Immune operator firstly uses to algorithm to transform all the paths to feasible paths; then the operator of chaotic mutation is used for global path planning.Prematurely and low convergent speed is the two fatal shortcoming of traditional evolution algorithm. In this paper, based on the shortcoming analysis above all, a bi-group evolutionary algorithm based on artificial immune and chaotic mutation is proposed. In this algorithm, evolutions of subgroups are parallel performed with different mutation strategy. One of the subgroups takes chaotic mutation operator to explore the solution spare separately, and the other subgroup searches the local part detailed using exponential descent operator. This algorithm could quickly plan global-optimal path. As it is proved by the results of the test, The simulation result confirms that the genetic simulated annealing algorithm is feasible and efficient for mobile robot path planning.