Research Of Genetic Algorithm And Ant Colony Algorithm In Robots' Path Planning

The research of the mobile robot begans in the late 1960s. It is the cross-product of the computer artificial intelligence, robotics, bionics, control theory, electronic technology and other technical disciplines. With the development of technology and advancement of society, the mobile robot has been piled out the laboratary. Many kinds of robots have been used not only wider and wider, but also deeper and deeper. The reserch of the mobile robots has been the trend, is a hot of the high-tech field.At present, the related studies of the mobile robot technology have made a lot of achievements, but there are many key theoretical and technical issues to be addressed and improved. Therefore, mobile machinery-related technology and mobile intelligent robots are all very practical significance.In this thesis, for the research assignment of the based mobile robot modeling and simulation, path planning for research, the main assignment is the application of intelligent optimization algorithms. In the first place, the history, classification, definition and technology of robots are introduced. The common used planning algorithm in the country and the broad, the research situation and the key technologies are elaborated and summaried, analyzing advantages and defaults of algorithm which made an important basis for the research on mobile robot path planning in the thesis, and kinds of traditional methods about mobile robot modeling and path planning, especially intelligent optimization algorithms, are recommended in the following words. In the next place, selection genetic algorithm and ant colony optimization in the path planning of the mobile robots. On one hand , The thesis has introduced the basic knowledge genetic algorithm, and according to the characteristic of path planning problem, every component of the algorithms are analyzed carefully, including environment representation, chromosome representation, path evaluation, genetic operators design and genetic algorithm parameters selection. A new hybrid genetic algorithm is presented by joint of genetic algorithm and Floyd algorithm. By optimizing the preliminary result got by Floyd algorithm, the best path can be got. The algorithm proves that it is better than a single algorithm, and it is very robust, fast optimization, convergence and so on. On the other hand, the ant colony optimization, The paper has analysed ant colony algorithm's principle, model, and the setting of the parameter especially. With rasterizing the environment the map can be got, it is used to approach for solving the path-planning problem in static and dynamic mobile robot environments and get preferable results simulation evaluation.