Innate immune based path planner of an Autonomous Mobile Robot

Abstract

Path planning of mobile robot is related to generating safest trajectories within its work space by avoiding obstacles, escaping traps and finally reaches its destination within optimal period. While an autonomous mobile robot is motion, each robot task needs a different form of learning because of its environmental changes. To select suitable robotic action at different environmental situations, a new motion planner is described in the current research work. The proposed motion planner is motivated from the biological innate immune system. To actuate a suitable robotic action, one new parameter named as learning rate has been introduced, which correlates the robot sensory information and the pre-engineered robot actions. The further movement of the robot is then decided by selecting of a suitable robot predefined task, there by the robot will move in sequence until it reaches to its destination. Finally, the developed path planner is executed in various simulated robotic environments.