Immunised Navigational Controller for Mobile Robot Navigation

Over the last few years, the interest in studying the Artificial Immune System (AIS) is increasing because of its properties such as uniqueness, recognition of foreigners, anomaly detection, distributed detection, noise tolerance, reinforcement learning and memory. Previous research work has proved that AIS model can apply to behavior-based robotics, but implementation of idiotypic selection in these fields are very few. The present research aims to implement a simple system architecture for a mobile robot navigation problem working with artificial immune system based on the idiotypic effects among the antibodies and the antigens. In this architecture environmental conditions are modeled as antigens and the set of action strategies by the mobile robot are treated as antibodies. These antibodies are selected on the basis of providing the robot with the ability to move in a number of different directions by avoiding obstacles in its environment. Simulation results showed that the robot is capable to reach goal effectively by avoiding obstacles and escape traps in its maze environment.