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UAV: Fixed wing aircraft

The purpose of this project is to develop an experimental testbed composing of a group of autonomous UAVs, wireless communication links, and a ground command station, which could be used for various applications, including: traffic, wild fire, and habitat monitoring in Civilian Applications; border surveillance and hazard monitoring in Homeland Security; and search and rescue and battle field surveillance in Military. Due to the wide scope of possible autonomous operations, the enormous potential of unmanned vehicles, especially low cost, small sized UAVs, is far from being fully realized.

In order to perform various missions, UAVs require enough payloads to carry sensors to utilize a real-time surveillance of dynamic environments and onboard computers for automated mission management. We have just begun to develop a testbed for autonomous multi-vehicle systems, which is composed of a UAV with 110 inches of wing span, carrying an onboard autopilot as shown in Figure 1, a ground command station, and a hardware-in-the-loop simulator shown in Figure 2. Once the testbed has been developed, the command generator (onboard computer) will be designed and algorithms for autonomous flights will be developed for various missions.

We will develop autopilots with automatic guidance, navigation, and flight control capabilities, which could perform automated visual search and mission management. The developed algorithms have following properties: robustness to uncertainty; adaptability to ever changing environments; real-timeness under the limit of sensing and on-board computing capability; scalability to the size of the agent network; and optimality with respect to certain performance indices.

Algorithm development for autonomous operations requires extensive knowledge about domain and environmental properties in conjunction with sufficient and accurate sensing and actuation capabilities. Knowledge from various research areas such as planning, real-time scheduling, natural language, computer vision, machine learning, and dynamics and control, must be combined to develop reliable autonomous systems.

Figure 1

Figure 2 

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