In real world applications such as rescue robots, service robots, mobile mining robots, and mine searching robots, an autonomous mobile robot needs to reach multiple goals with the shortest path while avoiding obstacles. In this thesis, we propose Artificial Immune System (AIS) based algorithms and two hybrids based on AIS associated with the Simulated Annealing (SA) algorithm and Voronoi Diagram (VD) for real-time map building and path planning for multi-goal applications. A global route is initially planned by the Immune System Algorithm (ISA). Then the created path is used to guide the robot to multiple waypoints following the foraging trail. An AIS-based point-to-point navigator is also proposed and tested here, which is used to navigate the robot along a collision-free global route. The proposed hybrid ISA model integrated with SA or VD algorithm aims to generate paths while a mobile robot explores terrain with map building in an unknown environment. We explore the ISA algorithm with simulation and comparison studies to demonstrate the capability of the proposed hybrid model of AIS and SA or AIS and VD algorithms in achieving a global route with minimized overall distance. Simulation and comparison studies validate the efficiency and effectiveness of the proposed hybrid models. They also confirm that concurrent multi-waypoint navigation with obstacle avoidance and mapping of an autonomous robot is successfully performed under unknown environments.