This study focuses on the implementation and demonstration of the Real Time Path Planner (RTPP). It is an AI guidance system that was developed for an operational DoD unmanned aerial target control system. The RTPP is tested using the 6-DOF target simulator of Drone Formation Control System (DFCS). The RTPP uses the A* algorithm to generate the obstacle free routes. The data used for the A* graph, which rep resents the terrain map, was obtained from the National Elevation Dataset. UAV flight trajectories developed b y this system undergo testing in a DoD flight simulator. A n MQM-107D simulated drone is used for the testing. The partition size of the terrain map is varied within the study to obtain an optimal partition size. Thes e tests analyze the flight trajectories produced by the RTP P, and the effect of varying the A* and the UAV parameters. This study demonstrates that the RTPP produces safe and maneuverable routes for the MQM107D. Results are confirmed by time plots of key fl ight parameters recorded during the 6-DOF UAV simulation runs. The time plots show that the targe t did not have any difficulty flying the computer-g enerated pattern. It also shows that the target flew over be nign terrain as predicted by the RTPP to reach its destination. This study demonstrates the innovative benefits and functionality added by Intelligent Sy stems theory to the real-time path planning and navigatio n task via the DFCS system. The RTPP works as designed, producing safe and flyable flight pattern s for the drone.