North-Up, Track-Up, and Camera-Up Navigation of Unmanned Aircraft Systems

To optimize UAV reconnaissance operations, direction of viewing and direction of travel must be allowed to diverge. Our challenge was to design a control and display strategy to allow the operator to easily look where they’re going, go where they’re looking, and look and go in different directions. Two methods of control were devised to align traveling forward, viewing forward and commanding forward. The operator can command the UAS to turn to camera or command the camera to point in line with the direction of travel (eyes forward). We have also introduced a new camera-up map orientation. The operator can easily cycle through North-up, track-up, and camera-up to provide the best link between the exo-centric and ego-centric frames of reference. Ego-centric and exo-centric perspectives allow the operator to combine or separate the vehicle’s movement and the camera’s view to optimize the search task while maintaining situation awareness of flight hazards

The Effectiveness of Augmented Reality as a Facilitator of Information Acquisition in Aviation Maintenance Applications

Until recently, in the field of Augmented Reality (AR) little research attention has been paid to the cognitive benefits of this emerging technology. AR, the synthesis of computer images and text in the real world, affords a supplement to normal information acquisition that has yet to be fully explored and exploited. AR achieves a more smooth and seamless interface by complementing human cognitive networks, and aiding information integration through multimodal sensory elaboration (visual, verbal, proprioceptive, and tactile memory) while the user is performing real world tasks. AR also incorporates visuo-spatial ability, which involves the representations of spatial information in memory. The use of this type of information is an extremely powerful form of elaboration. This study examined four learning paradigms: print (printed material) mode, observe (video tape) mode, interact (text annotations activated by mouse interaction) mode, and select (AR) mode. The results of the experiment indicated that the select (AR) mode resulted in better learning and recall when compared to the other three conventional learning modes