6.6 A COMPARISON OF AUTOMATED TAKEOFF AND LANDING SYSTEMS

Running head: A COMPARISON OF AUTOMATED TAKEOFF AND LANDING SYSTEMS 1
A Comparison of Automated Takeoff and Landing Systems Among
Manned and Unmanned Aircraft

Robert J. Winn
Embry-Riddle Aeronautical University-WW-ASCI638


Abstract
Since the beginning of aviation, man has continued to develop and improve automated aircraft systems intended to reduce the liveware workload associated with conducting all phases of flight operations. With the increased proliferation of unmanned aircraft systems (UAS) operating in excess of 24 hours and the capabilities of transport aircraft to engage in long-duration flight operations the necessity to reduce the workload of UAS operating crews has become more prevalent. This paper will analyze an automated system that is in use by both manned and unmanned flight operations during critical phases of flight. The automated system will be described as it relates to both operations and the capabilities and limitations of the system will be presented as well as its overall effects on safe operations. In closing, recommendations regarding the level of automation for future variants of the system will be presented.
Keywords, automation level, liveware, operations, safety, workload, NextGen

A Comparison of Automated Takeoff and Landing Systems Among
Manned and Unmanned Aircraft
The Federal Aviation Administration’s NextGen system was developed to modernize how the National Airspace System (NAS) is utilized. In order to allow more aircraft to operate within closer proximity to one another the aviation industry must implement automation which controls takeoff, landing and even ground taxi.
However, these automation systems are complex, and still require human inputs, even if just for programming. Contrary to their intention, this can actually increase pilot and controller workload as systems must be learned, programmed and monitored at all times for both manned and unmanned aircraft (Cooke, Pringle, & Pedersen, 2007)
This paper will provide a basic description of one of these systems and identify two platforms with which it is used. Capabilities and limitations of the system will be presented, as well as its effects Operational Safety and any possible recommendations for future enhancements.
Automated Takeoff and Landing Systems (ATLS)
Recent advancements in technology have allowed the NAS to be accessed by both manned and unmanned aircraft systems (UAS). Automated takeoff and landing systems or ATLS reduces the workload of the Air traffic Controller and of the flight crew during the most critical phases of operations; takeoff, landing and ground taxi. Two distinctly different operating platforms incorporate ATLS to reduce the flight crew’s workload during these most critical phases of flight, these platforms are the UAS Northrop Grumman Global Hawk and the manned air transport Boeing 777.
Global Hawk
The Global Hawk is a long endurance UAS capable of 32 hours of operation. The flight crews are invariably assigned to rotating shifts in order to accommodate these long flight hours and are most likely subjected to fatigue issues brought about by interruptions to their natural circadian rhythms. In an attempt to minimize the workload of the flight crews during the phases of fatigue, the Global Hawk is configured with satellite and line of site (LOS) data link control capability via the ground control station (GCS). The satellite link provides critical Global Positioning System (GPS) navigation data to the Global Hawk. Combined with a synthetic aperture radar moving target indicator (SAR/MTI) a high resolution electro-optical (EO) digital camera and a third-generation infrared (IR) sensor, all operating through a common signal processor making it capable of fully autonomous takeoff, flight and landing (Northrup Grumman , 2012).
This critical flight navigation data is used by the Mission Control Element (MCE) via the GCS and enables the flight crew to monitor all sensors, perform mission planning and if necessary change the autonomous flight operation using manual inputs and control. For the ground portions of its flight missions, including autonomous takeoff, landing and taxi operations the Launch and Recovery unit Element, or LRE, is used. This is primarily accomplished with its Differential Global Positioning System (DGPS) and with its LOS connectivity and operation capabilities (Northrup Grumman , 2012).
By means of this autonomous system the Global Hawk is the epitome of long-endurance flight operations, still capable of liveware intervention. 
Boeing 777
The Boeing 777 autonomous flight capabilities are in many ways similar to those of the Global Hawk and other UAV systems. The 777 is capable of autonomous takeoff, landing and flight, all with minimum pre-programming and inputs from flight crews (Boeing, 2014). In some aspects the infrastructure required for autonomous flight control of the 777 is not the same as that of the Global Hawk. Instead of a satellite and LOS command and control by means of the GCS the 777 takes advantage of its onboard flight crew and two primary autonomous systems; the Airplane Information Management System (AIMS) and the Electronic Flight Bag (EFB). By means of onboard sensors which provide critical system feedback of inflight controls and communication with ATC, the AIMS is capable of managing approach and departure procedures. The EFB minimizes crew workload for the majority of normal flight operations by automating checklists, flight plans and ATC approach information. By means of the EFB during the non-critical phase of operations the crew is less likely to have been subjected to workload fatigue and can focus more on the autonomous attributes of the ALTS during a flight critical phase.
Capabilities
By use of imploring autonomous control during critical phases of flight it removes the capabilities of error thru the liveware/hardware interface attributed to fatigue due to workload saturation.
Limitations
In any form, automation affects situational awareness by changing the operator’s role from actively controlling the system to passively monitoring the system (Endsley, 1996). When workload is reduced so is the operator’s situational awareness of the given task. To further compound task shredding via automation, should the automated task fail, the operator is less likely to successfully take control as the task has not been practiced and a complete understanding of the failure mode is unknown.
Without human input and intervention, the automated systems have no self-preservation motivations and can literally fly themselves into the ground, or follow unsafe inputs, simply because there is no reasoning of “this doesn't look right,” when an incorrect input or event is occurring in autonomous flight (Brown, 2015)
Operational Safety
The 777 ATLS are probably more likely to incur manual override than the Global Hawks as the crewmembers are one with the aircraft, not in a GCS, providing them with increased situational awareness. On the other hand, the Global Hawk pilot must rely on feedback from the UAV’s infrastructure to determine if there’s a problem that requires manual override. Using a fully autonomous system is designed to remove incorrect human pilot input errors that could cause unsafe flight conditions. However, as is illustrated in numerous manned and unmanned NTSB accident reports when autonomous systems were engaged, that is not entirely possible due to the human inputs necessary to program and design the autonomous software for flight ops in both manned and UAS systems (Cooke, Pringle, & Pedersen, 2007). Let’s not forget the adage, “A computer is only as smart as the operator”.
Training
Despite the incredible amount of automated flight capability in both unmanned and manned aircraft today, there is still a need for human insight and oversight to protect the machinery from itself when given faulty software or human inputs (Brown, 2015). To meet these challenges and to trust the capabilities the automated systems provide, those critical phases of flight must be part of simulator training so that the crews can recognize when automation is in error or has failed, allowing for manual control of the situation.
Level of Automation
Issues such as unbalanced workload, loss of SA, and skill loss can be addressed successfully by implementing Adaptive Automation (AA). Adaptive Automation is characterized by the ability to turn itself on in connection with a system or an operator event (Barnhart, 2011).
Current autonomous systems operate at a Level of Autonomy 1-3 (Low LoA) where the liveware (human) interface is the main component. As autonomous systems become more accepted by the liveware interface (operator) and user (passenger) these autonomous systems will progress in the near future to a level 7-9 (High LoA). At this level the system has very little interface with the liveware and no longer needs approval to execute its assigned operation/goal. The system will inform the human of its intent and will proceed unless there is human intervention.
Recommendation for Future Enhancements
Whereas AA is dynamic and flexible, traditional automation is static; total automation or autonomy is neither. At Beyond Level 10, autonomy or full automation proposes a strong artificial intelligence (AI) approach to automation. Humans have the unique ability to perform abstract judgment and reasoning tasks in undefined or ill-defined circumstances. So, it is unclear whether systems at a LoA 10 can be considered to be of human-level intelligence (Barnhart, 2011).

References
Barnhart, Richard K., Shappee, Eric, and Marshall, Douglas M.. Introduction to Unmanned Aircraft Systems. London, GBR: CRC Press, 2011. ProQuest ebrary. Web. 24 November 2015.
Brown, J. (2015) Automated Takeoff and Landing Systems in Manned and Unmanned Aircraft, Retrieved from http://www.droningonandon.com/blog/automated-takeoff-and-landing-systems-in-manned-and-unmanned-aircraft
Cooke, N., Pringle, H., & Pedersen, H. (2007). Human Factors of Remotely Operated Vehicles (Vol. 7). JAI Press.
Endsley, M. 1996. Automation and situation awareness, In Automation and Human Performance: Theory and Applications, ed. R. Parasuraman and M. Mouloua, 163– 181. Mahwah, NJ: Erlbaum.
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