Actual Number Of Immune Genetic Optimization Of Robot Path Planning

Path planning is the key technology of Mobile Robot research, the research on path planning is very important, especially when the hardware of robot cannot reach a high precision in a short time. The problem of real-coded Immune Genetic Algorithm (IGA) and its application in robot path planning is studied in this paper.Firstly, a kind of improved real-coded IGA was proposed benefiting from the thoughts of vaccine selection, vaccination, gene recombination, immune memory. Compared with GA and the existed Immune Algorithm, some modifications and improvements have been made mainly in such aspects as genetic selection, vaccine selection mode, vaccination mode. Proper testing scheme has been designed to test the performances of the improved real-coded IGA in this paper by utilizing three typical functions which are usually used to test intelligent optimization algorithms. The result shows that the algorithm has better improvements in convergence, search accuracy than GA with Elitism, and also keeps the diversity of evolving population. Based on the IGA, the new algorithm is applied to the robot path planning, adaptive parameters setting strategy is introduced, mutation operator is improved for the application of path planning, the experiment results demonstrate the algorithm can immediately find a path even in complex environment.Finally, for the problem of composition of forces amounting to zero and goal unreachable in path planning with artificial potential field, a method based on improved artificial potential field with IGA is proposed. The route safety and regulation frequency are introduced to the path fitness function, and the parameters of potential field model are optimized by IGA, so the deficiencies of artificial potential field model are overcome, and optimization of the route length and safety are realized, the simulation result shows that the proposed method can improve the performance of route plan effectively.