Automation of Air Traffic Management using Fuzzy Logic Algorithm to Integrate Unmanned Aerial Systems into the National Airspace

Unmanned Aircraft Systems (UAS) have been increasing in popularity in personal, commercial, and military applications. The increase of the use of UAS poses a significant risk to general air travel, and will burden an already overburdened Air Traffic Control (ATC) network if the Air Traffic Management (ATM) system does not undergo a revolutionary change. Already there have been many near misses reported in the news with personal hobbyist UAS flying in controlled airspace near airports almost colliding with manned aircraft. The expected increase in the use of UAS over the upcoming years will exacerbate this problem, leading to a catastrophic incident involving substantial damage to property or loss of life. ATC professionals are already overwhelmed with the air traffic that exists today with only manned aircraft. With UAS expected to perform many tasks in the near future, the number of UAS will greatly outnumber the manned aircraft and overwhelm the ATC network in short order to the point where the current system will be rendered extremely dangerous, if not useless. This paper seeks to explore the possibility of using the artificial intelligence concept of fuzzy logic to automate the ATC system in order to handle the increased traffic due to UAS safely and efficiently. Automation would involve an algorithm to perform arbitration between aircraft based on signal input to ATC ground stations from aircraft, as well as signal output from the ATC ground stations to the aircraft. Fuzzy logic would be used to assign weights to the many different variables involved in ATM to find the best solution, which keeps aircraft on schedule while avoiding other aircraft, whether they are manned or unmanned. The fuzzy logic approach would find the weighted values for the available variables by running a simulation of air traffic patterns assigning different weights per simulation run, over many different runs of the simulation, until the best values are found that keep aircraft on schedule and maintain the required separation of aircraft

Prospective Architectures for Onboard vs Cloud-Based Decision Making for Unmanned Aerial Systems

This paper investigates propsective architectures for decision-making in unmanned aerial systems. When these unmanned vehicles operate in urban environments, there are several sources of uncertainty that affect their behavior, and decision-making algorithms need to be robust to account for these different sources of uncertainty. It is important to account for several risk-factors that affect the flight of these unmanned systems, and facilitate decision-making by taking into consideration these various risk-factors. In addition, there are several technical challenges related to autonomous flight of unmanned aerial systems; these challenges include sensing, obstacle detection, path planning and navigation, trajectory generation and selection, etc. Many of these activities require significant computational power and in many situations, all of these activities need to be performed in real-time. In order to efficiently integrate these activities, it is important to develop a systematic architecture that can facilitate real-time decision-making. Four prospective architectures are discussed in this paper; on one end of the spectrum, the first architecture considers all activities/computations being performed onboard the vehicle whereas on the other end of the spectrum, the fourth and final architecture considers all activities/computations being performed in the cloud, using a new service known as Prognostics as a Service that is being developed at NASA Ames Research Center. The four different architectures are compared, their advantages and disadvantages are explained and conclusions are presented

Effects Of Video Game Playing And Training On Unmanned Aerial Vehicle Performance

The popularity of unmanned aerial vehicles (UAVs) has resulted in the need to determine who is suitable to learn to operate UAVs. The present study examined the likelihood that action video game players (VGPs) would make better potential candidates for learning to become UAV pilots. Additional training is also examined as a factor to determine how well training assists with maintaining situational awareness and vigilance during performance of the task, which are beneficial skills for UAV pilots to possess. Ninety-two undergraduate students participated in the study, and piloting skills were tested using the Multi-Attribute Task Battery-II, which consists of generalizations of piloting tasks. VGPs had superior performance on many of the tasks compared to non-video game players, and individuals that received training performed better than those that did not receive training. These findings indicate that VGPs may make a potential candidate group for UAV pilots without needing previous pilot experience

Artificial situation awareness for increased autonomy of unmanned aerial systems in the terminal area

Situation awareness is the human function of perceiving, comprehending and projecting the state of the environment which is of critical importance to the safe operation of aircraft. A highly autonomous Unmanned Aerial System (UAS) must replicate this behaviour in order to maintain an acceptable level of safety verses a manned vehicle. Nowhere in the flight is situation awareness more critical than during operation in the terminal area. Of primary concern during this stage of flight is the awareness of other traffic heading for the same airfield. This paper presents of a novel method of spatial projection of traffic vehicles encountered by an autonomous UAS in the terminal stage of flight. This projection method relies on a cooperative means of traffic perception, such as Automated Dependant Surveillance - Broadcast (ADS-B) and assumes there is a predefined route which vehicles follow through the terminal region. Whilst this is the case at the majority of airfield, traffic vehicles will not follow this path perfectly. This uncertainty in path following accuracy is captured by utilising a curvilinear reference frame and dealing with discrete transitions (such as the initiation of a turn) separately. It is shown that whilst this technique increases the computational complexity of the problem it can offer significant performance benefit

Future technological factors affecting unmanned aircraft systems (UAS):a South African perspective towards 2025

The fact that pilots are not physically situated in the aircraft for UAS operations makes the current standards applicable to manned aircraft not suitable for UAS operations (FAA, 2013). FAA (2013:18) states that ―removing the pilot from the aircraft creates a series of performance considerations between manned and unmanned aircraft that need to be fully researched and understood to determine acceptability and potential impact on safe operations in the NAS. According to ERSG (2013), not all technologies necessary to ensure the safe integration of civil UASs into civilian airspace are available today. The extrapolation that can be made based on the above arguments is that advancement of UAS technologies will more likely have a significant bearing on the safe integration of UASs into civilian airspace. Therefore, as an identified research gap, the research/main objective of this research is to identify future technological factors affecting Unmanned Aircraft Systems in the Republic of South Africa leading towards the year 2025

Distributing Non-cooperative Object Information in Next Generation Radar Surveillance Systems

Radar surveillance systems, in both airspace and maritime domains, are facing increasing challenges in dealing with objects that cannot be detected by traditional transponder-based radar surveillance technologies. These objects, including birds, weather, Unmanned Aircraft Systems (UAS), hot balloons, are labeled as non-cooperative objects. In order to prevent ambiguity and confusion for human operators using the surveillance data non-cooperative objects are commonly treated as unwanted clutter and removed from the displayed data. However, the omitted information of non-cooperative object can be critical to aircraft safety. With new developments in technology and radar capabilities, it is possible to detect these non-cooperative objects and consider how to distribute relevant information about them to human operators throughout a system. The research goal of this thesis is to identify the human factors challenges in future radar surveillance systems where non-cooperative object information is distributed to both air traffic controllers and pilots. In order to achieve the goal, the thesis first constructed a model of surveillance information distribution in current ATC operations and a model of surveillance information distribution in the expected future operational environment. The expected future surveillance information distribution model was then carefully examined to identify potential human factors challenges in the non-cooperative object information distribution process. Two of the identified challenges (non-equal time delay and information level of details) were studied in depth through conducting human-in-the-loop experiments and online surveys. The results of an asynchronous information (non-equal time delay) static simulation environment experiment showed that while a delay in the non-cooperative object information would lead to observable but not statistically significant longer communication time, it does have a significant effect on number of clarification statements – with an increase of time delay, more clarifications were made. A survey of controller and pilot perceptions of maximum acceptable delay showed no significant differences in the average maximum acceptable delay reported by controller (20.5 seconds) and pilot (13.64 seconds) participants. Future research should consider adopting dynamic simulation environment, subject matter experts and shorter delay intervals to identify an acceptable delay threshold. The survey results also demonstrated that there are more controllers and pilots who have had encounters with UAS in their daily tasks than what was originally expected. The survey also helped identify operational information requirements and availabilities for individual UAS and challenges in sharing non-cooperative object information between controllers and pilots. These findings are quite valuable as they provide guidance on future radar surveillance systems design in supporting the effective distribution of non-cooperative object information. Future work should complete the analysis of the survey and create more dynamic environment for studying information asynchrony