Unmanned Aerial Vehicle real-time guidance system via state-space heuristic search
Abstract
This Thesis focuses on the research, implementation, and empirical testing of a real-time (RT) Unmanned Aerial Vehicle (UAV) guidance system—the Real-Time Path Planner (RTPP). The RTPP was created for the following reasons: (1) proof-of-concept, i.e., to verify the real-world feasibility of developing a real-time guidance system by using AI techniques; (2) bench-marking, i.e., to create a platform for using the most promising Artificial Intelligence (AI) theories; (3) to show that a real-time guidance system is beneficial to existing Department of Defense (DoD) Target Command and Control Systems (TCCS). In this study, the RTPP undergoes empirical testing on an operational DoD TCCS called the Drone Formation Control System (DFCS). This testing is conducted by linking the RTPP to the DFCS real-time computer network. The RTPP provides flight-patterns to be used in 6-Degree Of Freedom (DOF) flight simulations by the DFCS Guidance, Navigation, and Control (GNC) systems. At present, guidance systems used by the DoD targets community in missions (i.e. the remote operation of aerial vehicles) require specially trained personnel to develop flight-patterns (i.e. precisely defined flight trajectories) prior to conducting missions. This process is constrictive and static because it forces missions to be performed as specified by these pre-launch generated flight-patterns. These limitations make missions extremely inflexible. This study proposes a solution that alleviates this problem. The solution is a real-time guidance system that allows DoD TCCS personnel (e.g. the project engineer) to make changes to flight profiles in real-time. The RTPP is a prototype of this solution. It provides a user-friendly computer interface for mission operators to safely guide and control target presentation locations in real-time. This easy-to-use interface is operated by using a mouse and keyboard to interact with its Graphical User Interface (GUI). The mouse can be used to select the flight trajectory's start and destination locations on the terrain map that is displayed within the GUI. The keyboard can be used for the same purpose but it is required for entering the other flight trajectory parameters (e.g., those associated with turn radius constraints). The user-entered UAV parameters are passed to the main module of the RTPP—the A* algorithm. Then the RTPP provides either flyable flight trajectories or no guidance at all (which occurs only when no solution exists). The returned flight-patterns have the attributes being flyable (i.e. minimal distance, and safe). The RTPP can be used in obstacle rich environments, provided the obstacles are static (e.g. mountains and other natural high elevation terrain). If the obstacles are man-made, their locations and MSL elevations must be entered into the RTPP. The RTPP uses files generated by the Terrain Map Creator (TMC), which is another product of this work. The TMC is a program that queries a high resolution environmental database for recording to a file the locations and elevations of the terrain over which the mission will be performed. In conclusion, the RTPP is used as the platform for testing the A* algorithm, which is the AI theory that is the recommended as the core technological solution. Its recommendation is verified by research, technical feasibility, analysis, empirical testing, and simulations. Hence, this Thesis demonstrates that DoD TCCS can benefit from innovations provided by AI theory.
Subject Area
Aerospace engineering|Artificial intelligence
Recommended Citation
Soto, Manuel, "Unmanned Aerial Vehicle real-time guidance system via state-space heuristic search" (2007). ETD Collection for University of Texas, El Paso. AAI1449746.
https://scholarworks.utep.edu/dissertations/AAI1449746