In order to operate UAVs under human supervisory control in more complex arenas such as urban environments, an obstacle detection system is a requirement to achieve safe navigation. The development of a system capable of meeting these requirements is presented. The first stage of development was sensor selection and initial testing. After this, the sensor was combined with a servomotor to allow it to rotate and provide obstacle detection coverage in front, below, and to both sides of the UAV. Utilizing a PC-104 single board computer running LabView Real-time for on-board control of the sensor and servomotor, a stand alone obstacle detection system was developed meeting the requirements of light weight, low power, and small size. The detection performance of the system for several parameters has been fully characterized. A human subjects study was conducted to assess the any advantages resulting from the addition of the obstacle detection system compared to that of a normal nadir camera. The study demonstrated that users with access to the three-dimensional display were able to navigate an obstacle course with greater success than those with only a camera. Additional development into more advanced visualization of the environment has potential to increase effectiveness of this obstacle detection system. / Master of Science

A system is described for identifying obstacle-free landing sites for a vertical-takeoff-and-landing (VTOL) semi-autonomous unmanned aerial vehicle (UAV) from point cloud data obtained from a stereo vision system. The relatively inexpensive, commercially available Bumblebee stereo vision camera was selected for this study. A "point cloud viewer" computer program was written to analyze point cloud data obtained from 2D images transmitted from the UAV to a remote ground station. The program divides the point cloud data into segments, identifies the best-fit plane through the data for each segment, and performs an independent analysis on each segment to assess the feasibility of landing in that area. The program also rapidly presents the stereo vision information and analysis to the remote mission supervisor who can make quick, reliable decisions about where to safely land the UAV. The features of the program and the methods used to identify suitable landing sites are presented in this thesis. Also presented are the results of a user study that compares the abilities of humans and computer-supported point cloud analysis in certain aspects of landing site assessment. The study demonstrates that the computer-supported evaluation of potential landing sites provides an immense benefit to the UAV supervisor. / Master of Science