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

Abstract

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.