News

Keep in touch with the latest news in the drones community

4papers

Well Clear, Simulations, UAS for Retail, Certifying Learning: four new papers

By | RPAS Chair | No Comments

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.

safetyfirst

Detect and Protect: security or safety ?

By | RPAS Chair | No Comments

As George Bernard Shaw once said “Both the optimists and the pessimists contribute to the society. The optimist invents the aeroplane, and the pessimist the parachute”. All over the world, numerous initiatives and demonstrations have shown the multiple benefits of drone operations, that is the optimists’ work. Now the pessimists are also at work to prevent malicious use of drones.

Read More
terrain_weather

Integrating terrain and weather avoidance to ACAS Xu

By | RPAS World News | No Comments

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.

aci

Airports will be ready for Drones, says ACI

By | RPAS World News | No Comments

The Airports Council International (ACI) recently published a position paper on Drone Technology giving an insight on their vision of the future. In this documents they acknowledge the important role that drones can play for the development of airport activities, the impact that drones traffic will have on airports, as well as the risks in termes of security and disruption of airport services.

The ACI asks for a common european effort, with a “no airport left behind” approach, and calls for cooperation with airlines, ANSPs and authorities, on topics including: the definition of restricted zones (geofencing), the detection and neutralisation of drones, and the definition of roles and responsibilities of the various actors. In this regard it strongly supports the U-Space initiative led by the SESAR-JU.

In terms of actions, the ACI World set up a “Drones Working Group” aimed at writing a Handbook and global guidelines for airports. At the same time, ACI Europe asks the EASA to write and publish a “European Safety Rulebook” to disseminate good practice and safety culture to the public. The ACI also acknowledge that a medium to long term integration will require to update relevant ICAO documents.

The envisioned roadmap for drones integration is to integrate the less risky operations as fast as possible, then define standard scenarios to enable operations in the EASA framework and finally gather from the aviation industry best practices and operational concepts.

In all the previous aspects, the ACI insists on the fact that any development must be “future proofed”, it is to say that it should be able to evolve as the technologies evolve.