The introduction of new CASs results in a multiplication of the number of different systems in the airspace. And this is not without consequences. During ACAS Xa integration tests, though the coordination was working perfectly, it was noted that the performances of TCAS were degraded. This highlighted the fact that CASs need not only to be able to coordinate but they also need to be interoperable.
In the beginning of this year, the Chair researchers have submitted four papers to highly rated conferences, but also to more specialized and industry oriented ones. Letters of acceptation for the four papers have been received recently, which means that the Chair will be able to present its work in four different forums. Here are the titles and abstract of the four papers:
Are You Clear About ”Well Clear” ? (to be published in ICUAS 2018)
Abstract—Regulations from the ICAO use the term Well Clear without defining it. Now, this definition is needed to design air traffic Detect And Avoid systems. A definition is currently discussed at the ICAO level, with work on the associated Remain Well Clear (RWC) function underway at standardisation bodies level (RTCA, EUROCAE). But many members of the communities impacted by these works are not well aware of their state. To adress this lack of awareness, this paper provides three contributions. First, it derives from ICAO texts the components of a RWC function: boundaries, alerts and guidances. These are linked to essential elements required to define the Well Clear term: a start and end, the actors involved, and the expected actions. Second, it summarizes the current regulatory efforts in RTCA, EUROCAE and ICAO regarding the Well Clear and Remain Well Clear notions. Third, it proposes discussion topics to move forward. From a DAA perspective, the notion of Well Clear is key to unlock RPAS full integration, i.e. operation in all classes of airspaces. Though existing works make good progress, the ressources engaged on this topic seem insufficient when compared with the complexity and importance of the task at hand.
An Introduction to Fast Time Simulations for RPAS Collision Avoidance System Evaluation (to be published in ICRAT 2018)
Collision voidance systems are crucial for RPAS integration, yet comparing their performances remain difficult. We believe that using fast time simulations and standard evalu- ation metrics would facilitate their comparison while providing insight into their benefits. However, fast time simulations are often viewed as hard to set up and limited to large scale demonstrations. We believe even small experiments can take advantage of them with huge benefits. The aim of this work is to ease access to fast time simulations by providing explanations, examples and references to previous works and to free software. We also list commonly used evaluation metrics for collision avoidance system performance ranking. By easing the setup of fast time simulation experiments, we believe future works will be able to provide their results in a more detailed and comparable form.
UAS Operations for Retail (to be published in ICAS 2018)
The number of UAS applications is quickly increasing as technology, standards and regulation allow them. With each new application, more industrial sectors get affected, and the retail sector is already being impacted. This paper presents five UAS applications that will impact the retail sector: freight, moni- toring, guiding, delivery, and advertisement. For each application, concepts of operation are provided along with the associated technological, standard and regulatory locks. These operations are then organized along time, from earliest to latest accessible, with accompanying explanation as to why and when. It is shown that the applications most publicized are not the ones that will come first. Finally, a discussion regarding the accuracy of our forecast is proposed and leads to support the enabling of drones are provided.
Machine learning for drone operations: challenge accepted (to be published in DASC 2018)
Machine learning is among the top research topics of the last decade in terms of practicality and popularity. Though often unnoticed, machine learning guides many aspects of our lives since its introduction via the big tech companies. Its abilities rise, defeating 9-dan Go professional, their accuracy increase, enabling smooth voice recognition, adding intelligence to our daily lives. However, its development is mostly supported by high tech companies rather than the public, or regulation, who show increasing concern about its usage. Despite some reluctance, machine learning has started to appear in aviation as well. Operational improvements were among the first applications. Recently an AGE sponsored competition for data scientists resulted in the first place being awarded to a routing algorithm providing a %12 improvement in fuel consumption by learning from real flight data. Other operational issues tackled by machine learning include accurate arrival time estimation and optimal take off parameters calculation. Because it originates from robotics, a part of the aviation community is particularly inclined to use machine learning: the drone community. In their search for autonomy, researchers from this community look for ways to apply machine learning to a core feature of aircraft: the avionics. However, strict regulation could limit these uses. For example, EASA drones regulation classify operations in different categories, depending on the risk. The most risky operations require avionics to be certified, which could prove tricky for non-deterministic machine learning methods. Apart from certification issues, how machine learning could be considered in risk analysis methods is also a question of interest. In this paper, we offer to present a classification of different machine learning algorithm families and consider their fitness for certification and risks analysis. For the relevant families, we discuss the enablers and try to understand the borders that might result or prevent the use of machine learning on certified safety systems, widely referring to the AIAA Roadmap for Intelligent Systems. Similar considerations are held for systems that do not require certification, but need to be taken into account in risks analysis methods. The ultimate purpose of this paper is to highlight the existing challenges which prevent machine learning algorithms from having a wider role in drone avionics, and more generally in aviation.
The presentation of these works will be an opportunity to share the Chaire’s ideas about drones integration and to get feedback from the community through discussion. We are expecting more papers to be ready by the end of this year.
According to the ICAO’s RPAS manual, a full Detect And Avoid (DAA) system must prevent collisions with: conflicting traffic, terrain and obstacles, hazardous meteorological conditions, ground operations and other airborne hazards (such as wake turbulence, birds and volcanic ash). However, most of the existing efforts focus on DAA for conflicting traffic as it represent the highest risk, letting aside the rest of the hazards. Especially in the case of ACAS Xu which design and evaluations focus on conflicting traffic avoidance.
Recently, Trustwave applied for a patent describing how to integrate existing terrain and weather avoidance systems with ACAS Xu. The goal being to inhibit collision avoidance maneuvers which could direct the RPAS into terrain or hazardous weather, and to account for these in the computation of Remain Well Clear (RWC) maneuvers.
The efficiency of such a system remains to be demonstrated, yet it is one step closer to a complete DAA system.
The RTCA Drone Advisory Committee (DAC) is a committee aimed at supporting the FAA on their regulatory effort to enable drone integration in the national airspace. The 8th of November, the DAC is meeting to consolidate their finding and reach consensus on the recommendations to provide to the FAA. This is likely to trigger from the FAA an update of existing regulation thus impacting the whole drone industry.
More information here.
The air traffic can be divided into cooperative and non-cooperative traffic. The cooperative traffic is equipped with avionics facilitating its detection. The non-cooperative traffic has no such equipment and detection is solely based on ground or onboard sensors. It is important to note that detecting cooperative traffic is a lot easier and more precise than detecting non-cooperative traffic. This is why many experts advocate for all low altitude traffic to be cooperative, at least in high density airspaces, and a proposed solution is to use ADS-B. This solution seem acceptable considering that a large part of the existing traffic is already required (or will be soon) to carry ADS-B out, the technical solutions exist and they are affordable both in terms of SWaP (Size, Weight and Power) and cost. Now, a crucial questions remains: is it possible to introduce hundreds of ADS-B users in already busy (radio frequency wise) airspaces without disturbing the performances of existing systems, e.g. ATM systems ?
To answer this question, the MITRE conducted a study on the impact of equipping low level drones with Universal Access Transceiver (UAT) ADS-B. Both air-to-air and air-to-ground communications were considered. According to this study the crucial parameters are the traffic density and ADS-B transmission power. The following table, extracted from the study, shows the probability to decode a message depending on drones density and transmission power with values in bold being acceptable for ATM applications. With a density of 5 drones per square kilometer the emission power cannot be higher than 0.01W, which strongly limits the communication range, though experiments to know the precise range depending on the transmit power should be conducted.
Overall, the results of this study show that using ADS-B UAT in high density airspaces will prove difficult has reducing the transmission power of ADS-B is likely to decrease detection ranges and impact safety. For the particular case of UAT, considering the fact that it is only used in the US, principally aimed at General Aviation (GA) and with the current grow in GA traffic, the FAA is unlikely to approve such solution to make the drones cooperative. From a broader perspective, the study showed how quickly a cooperative method can overload a communication mean. Having only cooperative traffic is desirable but this kind of study make it look like an unreachable objective. For now…
A recent report, from the John A. Volpe National Transportation Center, prepared for the FAA, presents an analysis of 220 reports of the Aviation Safety Reporting System (ASRS) related to UAVs. The ones filled by Air Traffic Control Operators (ATCOs) are of particular interest as they are crucial players for the integration of UAS in controlled airspaces.
On top of different statistics concerning the events, the report puts forward seven events particularly surprising for ATCOs:
- An unanticipated appearance of the UAV in the airspace;
- Difficulties to contact the UAS pilot;
- The UAV does not comply with pre-coordinated route;
- The UAV cannot accept (comply with) an instruction issued by the ATCO;
- The behavior of the UAV is unexpected; and
- The required actions for the controller are unknown or unclear.
When reading this report, keep in mind that most of these encounters imply military UAS pilots, which explains the high number of remote pilots disregarding ATC instructions.
This type of study is very useful when designing Real Time Simulations with ATCOs in the loop. Indeed, it allows creating worst case scenarios to experiment with ATCOs workload while representing realistic scenarios.