(Georgia Institute of Technology., 2012-11)
Watts, Robert; Christmann, Hans Claus; Johnson, Eric N.; Feigh, Karen M.; Tsiotras, Panagiotis
Handling en route emergencies in modern transport aircraft through adequate teamwork between the pilot, the crew and the aircraft’s automation systems is an ongoing and active field of research. An automated path planning aid tool can assist pilots with the tasks of selecting a convenient landing site and developing a safe path to land at this site in the event of an onboard emergency. This paper highlights the pilot evaluation results of a human factors study as part of such a proposed automated planning aid. Focusing on the interactions between the pilot and the automated planning aid, the presented results suggest that a particular implementation of the pilot aid interface, which uses a simple dial to sort the most promising landing sites, was effective. This selectable sorting capability, motivated by the anticipated cognitive mode of the pilot crew, improved the quality of the selected site for the majority of the cases tested. Although the presented approach increased the average time required for the selection of an alternate landing site, it decreased the time to complete the task in the case of emergencies unfamiliar to the pilot crew.
(Georgia Institute of Technology, 2012-11)
Chowdhary, Girish; Sobers, D. Michael, Jr.; Pravitra, Chintasid; Christmann, Hans Claus; Wu, Allen; Hashimoto, Hiroyuki; Ong, Chester; Kalghatgi, Roshan; Johnson, Eric N.
This paper describes the design and flight test of a completely
self-contained autonomous indoor Miniature Unmanned Aerial System (M-UAS). Guidance, navigation, and control algorithms are presented, enabling the
M-UAS to autonomously explore cluttered indoor areas without relying on any
off-board computation or external navigation aids such as GPS. The system uses
a scanning laser rangefinder and a streamlined Simultaneous Localization and
Mapping (SLAM) algorithm to provide a position and heading estimate, which is
combined with other sensor data to form a six degree-of-freedom inertial
navigation solution. This enables an accurate estimate of the vehicle
attitude, relative position, and velocity. The state information, with a
self-generated map, is used to implement a frontier-based exhaustive search
of an indoor environment. Improvements to existing guidance algorithms
balance exploration with the need to remain within sensor range of indoor
structures such that the SLAM algorithm has sufficient information to form a
reliable position estimate. A dilution of precision metric is developed to quantify the effect of environment geometry on the SLAM pose covariance, which
is then used to update the 2-D position and heading in the navigation filter. Simulation and flight test results validate the presented algorithms.