UAS Design in ARVL

            Designing a UAS takes a lot of carful consideration of the mission it will be asked to accomplish.  UAS can carry a multitude of payloads and sensors and can have a very wide performance envelope.  Because of these vast capabilities, one must be carful to not overdesign a UAS by adding features and components that are not required and that do not contribute to the mission.  For the crash lab mission that I selected I went through several design iterations to get the right combination of components which highlighted some of these challenges for me. 

UAS Design

            For my original design I started with the Gadfly Quadrotor base chassis, however I quickly realized that I was not able to fit the required components onto the design.  The chassis was not able to support a gimbaled camera or a camera with both Electro-Optical (EO) and Infrared (IR) spectrums.  Due to these limitations I was quickly forces to switch to the larger Condor Octorotor Professional base vehicle.  This chassis allowed for a gimbaled camera with both EO and IR capabilities to be fitted, as well as a Synthetic Aperture Radar (SAR).  For the ground control station, the Handheld controller was selected to allow for easy operation, minimal operator training and maximum portability.  While the ARVL controls were rather cumbersome to use, the real-world handheld controller would have been the perfect match for this system because it would allow for precise control of the UAS and the cameras fitted to the UAS (ERAU Hub, n.d.).

Performance

            Performance was an important aspect of this UAS design.  The initial design utilizing the Gadfly Quadcopter had the highest performance from both the speed and agility aspect and the battery performance aspect.  Infact, this high performance turned out to be somewhat of a challenge to control.  At maximum speed the Gadfly would reach an airspeed of about 40 (no units in ARVL).  This performance was just not necessary for the mission the UAS was being designed for and it increased the skill level required for the operator to ensure safe operation.  The first design change to the Condor Octorotor solved this problem by cutting the max speed in half and making the UAS much easier to control.  Unfortunately do to the increased power requirement from eight electric motors and increased sensor payloads, the battery life was cut from 18 minutes to 14 minutes.  However, due to the small size of the batteries this was not an issue because the operator could easily carry multiple batteries and change them out as required.  The final performance aspect was communication signal range.  The dipole antenna was selected on both systems, however range still seemed to be more limited on the Gadfly chassis.  This limitation was not an issue for the crash lab mission due to the short-range requirements.  The switch to the larger Condor chassis increased the range, but the change was almost un-noticeable (ERAU Hub, n.d.).

Mission

            The mission accomplishment forced one change to the UAS design.  The original design control scheme was automatic.  However, this present planning challenges due to the rudimentary planning software included in ARVL.  It was difficult to get the UAS into the right position and impossible to keep it there once it began the mission.  While the automatic flight planning did reduce the operator’s workload, making camera control easier, it made getting the right shot for the mission almost impossible.  For this reason, the second major design change was from automatic control to manual control.  This allowed the operator to fly the UAS into the proper position, allow it to automatically hover in position and then adjust the camera as required for the images they needed to capture.  This did increase operator workload, but it was essential to ensure mission accomplishment.  The only other change that as made during the mission testing phase was that the operator view had to be changed from the pilot perspective to the onboard perspective.  This was due to the UAS being to small to see clearly at the distances it was flying from the operator.  It was impossible to determine orientation and camera angles without the onboard view (ERAU Hub, n.d.).

In the end the UAS I designed was able to accomplish the mission with no limiting factors or problems.  While it took multiple iterations to the design, this was expected and had it not occurred it would have been due to pure luck, as modifications are always expected when designing a new system.

References:

ERAU Hub [Computer software]. (n.d.). Retrieved September 28, 2018, from https://erau.instructure.com/courses/84078/pages/resources-virtual-hub?module_item_id=4529679

UAS Human Factors

            Human factors are a multi-faceted issue that is faced by machines such as cars, aircraft, ships, etc.  Unmanned aerial systems are no exception, and in many ways suffer from some of the toughest human factors challenges.  According to Howe (2017), “human factors is the multidisciplinary study of human capabilities and limitations…applied to equipment, systems, facilities, procedures, jobs, environments, training, staffing, and personnel management…for safe, comfortable, and effective human performance.”  Translation: human factors is the study of how man and machine work together and how to improve the way a human’s interface with the machine to operate it safely and effectively.

            One of the biggest areas of study for human is how automation can be utilized to improve the working relationship between the human and the machine.  There are many ways that automation can increase safety, such as reducing workload and thus decreasing stress and fatigue, increasing an operator’s ability to monitor and think about what they are doing, which in turn increases safety and reduces the chances of human error occurring.  Of course, these benefits do not come without concerns.  Automation, if used improperly, can lead to complacency and over-reliance as well as misleading indications or warning in the event of malfunctions (Howe, 2017).  One of the other main human factor challenges that unmanned system face is the separation of the operator from the vehicle they are controlling.  This leads to the loss of most sensory cues, generally only leaving visual cues for operators to rely on.  There are solutions to this challenge by using haptic feedback systems and auditory cues, but many unmanned systems have yet to incorporate them into their designs (Landry, 2017).

            These challenges prevent a unique but very interesting hurdle for unmanned systems to overcome.  The bottom line is that human factors design is a balancing act of information.  To much information and the operator could become task saturated and overwhelmed leading to mistakes or things being missed.  To little information (or to much automation) and operators could be uninformed about what their system is doing which could lead to becoming complacent or unaware of malfunctions that are occurring.

References

Howe, S. (2017, June). The Leading Human Factors Deficiencies in Unmanned Aircraft Systems. Retrieved from https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20170005590.pdf

Landry, S. J. (Ed.). (2017). Handbook of human factors in air transportation systems. Retrieved from https://ebookcentral.proquest.com