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An Accelerated Dual Fast Marching Tree Applied to Emergency Geometric Trajectory Generation
No full textInternational audienceThis paper addresses the generation of aircraft emergency trajectories with obstacle avoidance. After presenting in detail the fast marching tree algorithm, in this paper we propose an improvement of its performance. First, the free space checking function is sped up. Then, the algorithm is used twice, firstly with the sampling of a few points to generate an approximate trajectory, and secondly with a sampling of points close to the first computed trajectory to refine it. The proposed method significantly reduces the computing time of the emergency geometric trajectory generatio
Optimal Contract for Reducing Flight Delays in EU: In the Context of SESAR
No full textInternational audienceIn the context of the SESAR (Single European Sky Air traffic management Research) Joint Undertaking, the role that the air navigation service provider (ANSP) could play has been reconsidered. ANSP manages traffic and deals with potential conflict situations and external events, which have led to the reorganization of the air traffic. The modification of traffic inevitably leads to delays for airlines, which is costly. In this study, we suggest that ANSP could provide a costly delay reduction service to airlines. Indeed, if ANSP has several solutions for traffic conflict resolution or reorganization, there is room for a choice between these solutions using additional criteria. Our study thus proposes an original model in which we determine the optimal design of a delay reduction contract signed between welfare- or profit-maximizing ANSPs and a monopoly airline. We give some comparative statics, in particular, the evaluation of the impact of a modification in safety standards on the contract
ASRS-CMFS: Using a custom Transformer-based model to predict anomalies in aviation incident reports
No full textInternational audienceIn this article, the authors built and used a custom transformer-based model, based on a compact version of RoBERTa, named ASRS-CMFS, to classify aviation incident reports. The classification is applied to fourteen distinct sets of specific aviation incident-related anomalies, such as Aircraft Equipment problems or Altitude Deviation problems. The authors extracted the incident reports and the associated fourteen sets of categories from the Aviation Safety Reporting System. After discussing the choice of evaluation metric, the authors evaluated the model using the Matthews Correlation Coefficient metric. To measure the precision of the scores obtained on the different text classification problems, the authors provided the results with confidence intervals. They also used statistical hypothesis testing to evaluate the impact of the document length on the performance of the custom model. The authors provided a mathematical demonstration for the use of confidence intervals and hypotheses testing on MCC values. Finally, the authors discussed whether the model was fit for use in a professional environment. The authors found that while the model showed promising results, this question could only remain unanswered at this stage, but the steps to take are clear. The authors also proposed that hypothesis testing could 1 be valuable in any situation where one wanted to study the impact of a particular document feature on the performance of a document classifier
Formal Verification for Autopilot - Preliminary state of the art
No full textThis document is a preliminary state of the art for the formal verification of the autopilot of an Unmanned Air Vehicle (UAV). We will first present UAV autopilots and more specifically the Paparazzi autopilot developed at ENAC which will be our case study. We then present which properties could be verified and on which representation of the autopilot (source code, model). A more complete state of the art of current formal methods will be then detail and focus on deductive methods, abstract interpretation, model checking and proof assistants. Finally, some immediate perspective for the thesisare proposed
Experimental Evaluation of Robustness of Panel-Method-Based Path Planning for Urban Air Mobility
No full textInternational audienceIn this study, robustness of panel method based path planning algorithm under wind disturbances is evaluated experimentally. Panel method, borrowed from fluid dynamics domain, is a numeric tool for calculating the potential field around arbitrarily shaped objects. Resultant potential field can be used for generating collision free trajectories for uncrewed aerial vehicles with convergence guarantee. Robustness of the proposed method is demonstrated during indoor experiments with a wind generator creating wind speed up to 7 m/s and also during outdoor experiments with wind speeds ranging between 3 - 5 m/s. Experiment results suggest that panel method based path planning scheme maintains its obstacle avoidance property under wind disturbances
Environmental Impact Optimisation of Flight Plans in a Fixed and Free Route network
No full textInternational audienceEfforts to minimise the environmental impact of aviation can be implemented at several levels including electric green taxiing systems, novel propulsion systems, jet fuels, improvements in aircraft efficiency and optimisation of climb profiles. In this paper, we address the optimisation of flight plans in a route network considering operational parameters and weather forecast. We implement an A* based approach to explore all possible sequences of nodes, altitudes with given wind, temperature and pressure forecasts in order to minimise total flown distance, total burnt fuel, CO2 and non-CO2 emissions, the latter being responsible for two-thirds of aviation radiative forcing. We evaluated our approach on both a network of standard routes and through free route areas, and observed that our optimised flight plans are consistent with those that have actually been filed to be flown in similar weather conditions during December 2021
A Machine Learning Framework to Predict General Aviation Traffic Counts A Case Study for Nice Cote D'Azur Terminal Control Center
No full textInternational audienceGeneral Aviation traffic prediction is a major concern for Air Navigation Service Providers as it has a direct impact on air traffic flow and capacity management measures. However, today, few tools are available to address this issue. This paper proposes a methodology to predict GA traffic based on Machine Learning models training with historical data. Initial promising results are obtained on Nice Cote D'Azur Terminal Control Center sectors case study using meteorological and calendar data with an increase of the prediction performance of 25% compared to current tools used in operation
Slot allocation in a multi-airport system under flying time uncertainty
No full textInternational audienceSlot allocation in a single airport aims to maximize the utilization of airport declared capacity, while slot allocation in a multi-airport system (MAS) has to take airspace capacity into account. Because the limited capacity of certain departure/arrival fixes in the terminal airspace can cause unnecessary flight delays. The uncertainty of flying time between airport and congested fixes makes it even more complicated for slot allocation in a MAS. Traffic flow may be over capacity when the flying times of flights change. In this paper, we propose a mixed integer-programming model for slot allocation in a MAS. The objective of the model is to minimize the total displacements of flights in the MAS while considering all the capacity constraints as well as the uncertainty of flying time. The constraints at departure/arrival fixes are transformed into chance constraints, and Lyapunov theorem is applied for the transformation. To test the proposed model, a case study of schedule optimization in the MAS of Guangdong-Hong Kong-Macao Greater Bay is presented. Specifically, the impact of the uncertainty of flying time from five airports to airspace fix YIN is investigated. Results show that the total displacements increased if the uncertainty of flying time was considered. The optimized schedule, however, is more robust which can satisfy capacity constraints in various scenarios
Attention Networks for Time Series Regression and Application to Congestion Control
No full textInternational audienceThis paper studies a new attention-based recurrent architecture, lighter and less computationally expensive than a global attention network. This type of architecture achieves better performance than commonly used recurrent networks for time series regression. An application to congestion control is considered, where the history of round trip times (RTT) evolution history is used to monitor congestion control. The performance of the proposed new congestion control strategy is evaluated with both synthetic and real traces, showing that it can be efficiently used to estimate the congestion state of a network
A Lightweight Fluid Model for Mobile Ad hoc Distributed Systems
No full textInternational audienceEmerging edge applications introduced new computing timevariant topologies with mobile nodes connected via ad hoc networks. Such topologies are fundamentally different from cloud infrastructures due to the lack of hierarchy and clear network function separation. Sometimes, nodes that are sources are also destinations and routers, hence, creating dynamic flow patterns traversing the network. The latter, thus, can change the average performance of a distributed system, such as throughput and latency. This work introduces an analytical model based on fluid quantities to study data flows of distributed systems in mobile ad hoc networks. Using an approach based on a network of queues with evenly distributed bandwidth over concurrent flows, this lightweight model enables fast, coarse-grained analysis of different distributed systems configurations. They enable the analysis of different topologies, mobility and data flow models with a small footprint. The model was implemented, validated and evaluated with stress workloads to confirm its accuracy