Path Planning For Mobile Robot Based On Immune Evolution Algorithm And Coevolution Algorithm

The research and development of mobile robots have been paid great attention to in recent years. The requirement of its ability to take corresponding measures in accordance with the environmental changes in its moving courses has become higher and higher. Therefore, the path planning, as an important field in robot intelligence, appears particularly significant. Path planning is searching a optimal path without touch form the start to the end under way a performance indicators. The author mainly focuses on the study of the path planning of mobile robots and its relevant problems. Following are the main content of this thesis:Firstly, this dissertation introduces the research background and status quo of path planning and relative theory. Afterwards, a new robot path planning method and a new multi-robots path planning method proposed by author and integrated by the virtue of immune operation and coevolutionary.Considering the drawback of easily being trapped in a local solution and lack of guidance of traditional evolution algorithm on single-robot path planning, a new robot path planning method based on immune evolution algorithm has been discussed. This method introduce the immunity operation and optimize the operator which can generate good individuals earlier and accelerate the algorithm's convergence .At the same time, it can maintain diversity of the antibodies and improve global search ability. The results of simulation show that method has better adaptive ability towards complex maps and it is feasible and efficient for improving the performance and quality of path planning for mobile robot.According to the feature of multi-robots path planning and the coevolutionary model, a new multi-robots path planning method based on immune coevolution algorithm has been discussed. it can prevent multi-robots collision when keep high quality path planning result to the every single path. From the results of simulation and the multi-robot parking experiment, we can conclude this method is high efficient in performing the multi-robots path planning.