A Real-time Global Optimal Path Planning for mobile robot in Dynamic Environment Based on Artificial Immune Approach

This paper illustrates a method to finding a global optimal path in a dynamic environment of known obstacles for an Mobile Robot (MR) to following a moving target. Firstly, the environment is defined by using a practical and standard graph theory. Then, a suboptimal path is obtained by using Dijkstra Algorithm (DA) that is a standard graph searching method. The advantages of using DA are; elimination the uncertainness of heuristic algorithms and increasing the speed, precision and performance of them. Finally, Continuous Clonal Selection Algorithm (CCSA) that is combined with Negative Selection Algorithm (NSA) is used to improve the suboptimal path and derive global optimal path. To show the effectiveness of the method it is compared with some other methods in this area.